Socioeconomic criteria for defining farmer recommendaton [i.e. recommendation] domains:

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Socioeconomic criteria for defining farmer recommendaton [i.e. recommendation] domains: implications for farming systems research
Kelly, Terry C
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viii, 107 leaves : illustrations. ; 28 cm.


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Agricultural systems -- Research ( lcsh )
Agriculture -- Research ( lcsh )
bibliography ( marcgt )
non-fiction ( marcgt )
Academic theses ( lcgft )
Academic theses ( fast )


Thesis (M.S.)--Colorado State University, 1984.
Includes bibliographical references (leaves 94-101).
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submitted by Terry C. Kelly.

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Submitted by
Terry C. Kelly

Department of Agricultural and- Natural Resource Economics

In partial fulfillment of the requirements

for the Degree of Master of Science
Colorado State University

Fort Collins, Colorado Summer, 1984




Submitted by

Terry C. Kelly
Department of Agricultural and Natural Resource Economics

In partial fulfillment of the requirements

for the Degree of Master of Science

Colorado State University

Fort Collins, Colorado Summer, 1984


May 9, 1984


ENTITLED Socioeconomic Criter ia for Defining Farmer Recommendation

Domains: Implications for Farming Systemis Research


Committee on Graduate Work

Department Chairmnan




Successful farming systems research and development requires

that relatively homogeneous groups of farmers for whom more or less the same recommendations can be made be identified. Such groups are called recommendation domains. This thesis examines the recommendation domain issue with the intent to suggest means of incorporating appropriate socioeconomic variables into the stratification procedure. The hypothesis is that incorporating socioeconomic and cultural factors into famer stratification increases the likelihood that research recommendations will be successfully adopted by client farmers.

An assertion is made that those factors which most influence farmers' decisions regarding adoption of agricultural technologies

are the factors which should be considered when stratifying farmers. Twelve key socioeconomic factors are identified on this basis, with others of lesser importance also included in the discussion. These factors are then incorporated into a suggested procedure for famer stratification. A case using actual data from Lesotho illustrates the viability of this stratification procedure.

Terry C. Kel ly
Department of Agricultural and
Natural Resource Economics
Colorado State University
Fort Collins, Colorado 80523
Summer, 1984



I especially want to thank my wife and colleague, Judith Kidd, and our young son, Nathan, for being so supportive and understanding,

particularly during those times when I neglected my familial responsibilities. They reminded me that there is life beyond graduate school.

I also with to thank my advisor, Jerry Eckert, and my committee

members, Willis Shaner and Ronald Tlnnermeier, for their support, for their ideas and criticisms, and above all, for the flexibility they maintained through the ups and downs of this research. A special thanks goes to Kathy Fiddler who worked beyond the call of duty to process these manuscripts.

Finally, I'd like to express my appreciation to the United States Agency for International Development (USAID) and the International Scholarship Program at Colorado State Unviersity for providing funding for this research through USAID's Memorandum of

Understanding with CSU.



2 7

ORIENTATION OF THE STUDY . . . . . . . . . . . . . .

1.1 Introduction . . . . . . . . . . . . . . . . .

1.2 A Farming Systems Research Orientation

1.3 Why Define Recommendation Domains? . . . . . .

1.4 Emphasis on the Economic, Social, and Cultural

Aspects . . . . . . . . . . . . . . . . . . . . . . .

1.5 Proposed Methodology . . . . . . . . . . . . . . . .

2. THE EVOLUTION OF STRATIFICATION . . . . . . . . . . . . .

2.1 Classifying Farming Systems . . . . . . . . . . . . .

2.2 Stratification Emphasizing Technical Criteria . . . .

2.3 Stratification Emphasizing Human Criteria . . . . . .

2.4 Summary of Stratification Procedures . . . . . . . .

2.4.1 Household Resorce Base . . . . . . . . . . . .

2.4.2 Cultural Practices . . . . . . . . . . . . . .

2.4.3 Institutional Characteristics . . . . . . . .

2.4.4 Household Characteristics . . . . . . . . . .


3.1 Methodological Considerations . . . . . . . . . . . .

3.2 Community Environment . . . . . . . . . . . . . . . .

3.2.1 Market access . . . . . . . . . . . . . . . .




3.2.2 Access to other institutions . . . . . . . . . 41

3.2.3 Ethnic or class differences . . . . . . . . . 44

3.2.4 Population densities and local employment

characteristics . . . . . . . . . . . . . . . 45

3.2.5 Group interactions . . . . . . . . . . . . . . 46

3.3 Farm-Firm Characteristics . . . . . . . . . . . . . . 47

3.3.1 Labor . . . . . . . . . . . . . . . . . . . . 47

3.3.2 Land . . . . . . . . . . . . . . . . . . . . . 50

3.3.3 Capital equipment . . . . . . . . . . . . . . 51

3.3.4 Cash and income . . . . . . . . . . . . . . . 52

3.3.5 Risk factor . . . . . . . . . . . . . . . . . 53

3.3.6 Interactions within the farming system . . . . 54

3.4 Household Characteristics . . . . . . . . . . . . . . 55

3.4.1 Household composition . . . . . . . . . . . . 56

3.4.2 Household goals . . . . . . . . . . . . . . . 58

3.4.3 Household orientation . . . . . . . . . . . . 59

3.4.4 Other household characteristics . . . . . . 61

3.5 Checklist of Socioeconomic Factors . . . . . . . 62


STRATIFICATION . . . . . . . . . . . . . . . . . . . . . . 67

4.1 A Procedure for Socioeconomic Stratification . . . . 67 4.2 The Case of Lesotho's Lowlands . . . . . . . . . . . 74

. vi.




5. SUMMARY AND CONCLUSIONS . . . . . . . . . . . . . . . . . 87

5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . 87

5.2 Conclusions . . . . . . . . . . . . . . . . . . . . . 89

5.2.1 Policy Implications . . . . . . . . . . . . . 91

5.2.2 Further Research Directions . . . . . . . . . 9.

BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . 94



Tabl e P

1 Socioeconomic Checklist for Farmer Stratification.


Fi gu re

1 The hierarchical relationships among regions, farms

and agroecosystems . . . . . . . . . . . .

2 Procedure for Socioeconomic Stratification . . . . . . .

3 Stratification of rural households in the central

lowlands, Lesotho . . . . . . . . . . . . . . . . . . . .




1.1 Introduction

It is generally agreed that sustained improvement in the prosperity of a society requires the active participation of the impoverished majority. In Low Inccrne Countries (LICs), at least, the greatest number of poor are rural and depend primarily upon low

resource, but efficient and canplex farming systems to meet their everyday needs. A necessary condition for amelioration of life in a society is improvement of these small farm systems. Farming Systems Research has recently been advanced as an approach toward this end.

Since such research is fairly site-specific, it is necessary to identify groups of farmers for whom research results generated by FSR methodologies are applicable. Such groups, or recommendation domains,1 have traditionally been defined on agro-ecological bases with obvious

institutional differences such as farm size or market access only occasionally considered. More recently, the development community has recognized the influence of economic and social factors on farmers' decisions. Consequently, socioeconomic factors have become increasingly important in all phases of agricultural research, including delineation of recommendation domains. However, no commonly accepted framework

1 Byerlee, Collinson, et al. (1980) define a recommendation domain as a "group of relatively homogeneous farmers with similar circumstances for whom we can make more or less the same recommendations."

exists for this stratification process. This research examines the

small-farmer stratification issue with the intent to suggest means of incorporating appropriate socioeconomic variables.

More specifically, this thesis proposes to 1) review and codify current approaches to stratification, 2) identify those socioeconomic factors which are critical to differentiating farming systems under various circumstances, and 3) develop guidelines for incorporating these important socioeconomic factors into the basis for stratifying farmers into homogeneous subgroups.

1.2 A Farming Systems Research Orientation

Recent attempts to address the problems of small farmers in LICs

have resulted in an applied methodology called Farming Systems Research

(FSR); its various aliases include Farming System Approach to Research (FSAR), Farming Systems Research and Development (FSR&D), and Farming Systems Research and Extension (FSR/E). Farming Systems Research is thought of in two modes, "upstream" FSR and "downstream" FSR. Shaner, et al. (1982, p. 37) characterize "upstream" FSR as being "partly basic, broadly general, and supportive;" whereas "downstream" FSR is "site specific, primarily adaptive, and useful without long delay for target groups of farmers." Target group stratification is employed

primarily in "downstream" Fanning Systems Research. For the purposes of this thesis, then, FSR is defined generally to refer to downstream,

adaptive on-farm research that views the farm household and the activities managed by it as a whole. FSR begins with farmers' problems as perceived by farmers. Research is then designed in part by farmers,

managed (again in part) by farmers, and adjusted as farmers' circumstances dictate. In other words, client-farmers are involved actively throughout the entire research process. This study is conducted within the context of this FSR methodology.

Typically, the clients of FSR are small subsistence farmers. A small farmer is on e whose farm has a very limited resource base, rather than just being small in land area. "Subsistence" means that a larger proportion of output from farming and household operations is retained for the household rather than sold or exchanged in the market place. Subsistence farms are generally characterized by 1) limited involvement in both the consumption and production sides of the wider economy, 2) employment of mostly traditional technologies, and 3) a close relationship between sociocultural considerations and
household decision-making (Wharton, 1969) . These farms are first a home and, second, a business. In fact, farming may not be the most important or highest priority activity of the household. Thus, farming decisions are made for other than just profit maximization and agronomic reasons, an important point for researchers to remember.

When discussing a farming system, we are referring to a "unique and reasonably stable arrangement of farming enterprises that the household manages according to well defined practices in response to the physical, biological, and socioeconomic environments and in accordance with the household's goals, preferences, and resources" (Shaner, et al. 1982, P. 16). It is important to add that "farming

2 Wharton develops a more rigorous characterization of subsistence farming based on various economic, sociocultural, and developmental criteria.

enterprises" include all household enterprises, some of which are not specifically farming. Herein lies the strength of FSR: it concentrates not only on specific enterprises and activities but also on the interrelationships among all the farm/household activites. Hence, FSR takes a holistic view of the faming household.

A farm system is really an arrangement of component subsystems which function as a unit (Hart, 1980). These subsystems include a socioeconomic system as well as any number of agroecosystems, each of which is also an arrangement of subsystems such as soil, crops, weeds, insects, animals, etc. The farm system is itself a subsystem

of a larger regional system, and so on (Hart, 1980). (see Figure 1)

FSR as a methodology is often characterized by four stages:

1) the diagnostic stage, 2) the design stage, 3) the testing stage, and 4) the extension stage (Norman, 1983b). Shaner, et al., (1982) divide the FSR process into five basic activities: 1) target and research area selection, 2) problem identification and development of
a research base, 3) planning on-farm research, 4) on-farm research and analysis, and 5) extension of results. Activities 1 and 2 are roughly equivalent to Norman's diagnostic stage, and this is the stage of primary concern for this study. A valuable attribute of FSR is that it is adaptable as it proceeds. While preliminary recommendation domains are delineated early in the diagnostic stage (or in activity 1 of Shaner's division), as more knowledge of the area and the faming systems is gathered, the recommendation domains are

further refined.

Source: Hart (1980)

Figure 1. The Hierarchical Relationships Among Regions, Farms,
and Agroecosystems.

In the initial phase of FSR, the first step is to gather and

collate data on the target area 3 and farmers' circumstances. Byerlee, et al. (1979) define farmer circumstances as all those factors which bear on farmers' decisions with respect to technology. They include natural, technical, economic, and sociocultural factors. An initial delineation of recommendation domains is done at this time. Analysis of secondary data is usually followed by an informal or exploratory survey (sometimes called a sondeo) which may then be followed by a verification survey. At each step the recommendation domain boundaries are refined as necessary. The purpose of these steps is to gain as complete a knowledge of the farmers' circumstances as possible in as short a time as possible. This information is needed to identify the appropriate target groups and to design their respective research activities.

Some final notes on Farming Systems Research need to be made at this point. Its "bottom up" nature requires that it be collaborative and multidisciplinary. Economists and other social scientists are as important to the development of the small farmer as are technical

scientists. Second, because survival is paramount in their decision making, subsistence farmers have a very low risk threshold and, hence, will change only in increments and then, only if their survival is not further threatened. FSR seeks to identify such incremental
improvements to farming systems, instead of jumping to agronomically and economically optimal solutions or packages. Third, because of

3 Target area is a research area usually designated for policy
reasons. This is different than target group or recommendation domain.

this critical situation in which most subsistence farmers find themselves, FSR looks for immediate or short term applications to help alleviate farmers' problems. However, the long term implications of any recommendation cannot be ignored; the "upstream" mode of FSR is oriented more to deal with long-term issues. Finally, since FSR, as used here, concentrates on the short term, many institutional arrangements, social relationships, etc. which are changeable in the long run by policy makers and/or farmers are treated as fixed parameters. Only those circumstances which can be changed immediately or in the near future are considered variables in Farming Systems Research.

1.3 Why Define Recanmendation Domains?
Research designed to benefit a particular client group would be

considered successful if results of the research, eg. recommended technologies, were extensively adopted by the clients. To help

insure successful adoption of technology, recommendations must address client problems. It is, therefore, necessary to identify client groups that have the same or similar research needs. Collinson (1982, p. 8) correctly stated that, "Adaptive on-fam research can only be done effectively with a particular farm situation, and therefore an identified target group of farmers, in mind." Ideally, since
each farm system is unique, research should be performed on an individual farm basis. But of course, resources available for research are not unlimited, making this option unrealistic. On the other hand, research directed at a large number of diverse farmers would

most likely result in low acceptance rates of the recommended technologies, again wasting precious research resources. A recommendation domain approach to research is therefore a compromise. Farmers in a recommendation domain should have the same researchable problems and development alternatives, and should react in similar ways to policy and technological changes (Government of Zambia, 1979; Gilbert, et al., 1980).

In addition to being necessary for successful on-farm research, recommendation domains are important for at least two other reasons.

First, correct identification of a domain's circumstances can allow the research results to be extended to all clients within the domain as well as extrapolated to farmers with similar circumstances outside the research area. This could significantly improve the cost effectiveness of adaptive on-farm research by expanding the universe for which the research results are applicable (Harrington, 1980). Benjamin (1980) argues that it is more cost-effective to identify specific recommendation domains and extrapolate than to aggregate. Second,

stratification will help to ensure that local circumstances and interests are considered when planning research and formulating

policy (Goverrnent of Zambia, 1979). Policy makers can use this information to help determine priority groups toward which to focus their research efforts (Franzel, 1981).
Stratification of farmers in a target area is essential to

ensure that all farmers in a target area are identified. Adaptive on-farm research raises the opportunity cost of neglecting farmers

outside the specific target group (Gilbert, et al., 1980). Too often

farmers are omitted from the research because they are not "representative" of the predominant farming system in the area. Identifying

these farmers as a separate domain ensures that they will at least be considered when research funds are distributed, not simply neglected because they have not been identified.

Behnke and Kervin (1983) present a problem with stratification

which researchers must consider. Primarily from their experience in Botswana, they argue that grouping farmers into homogeneous units may hide or obscure the economic and social interdependencies of the community under study. Within a community, target groups will, in fact, represent economic classes based on differential access to the means of production. Farmers with quite different but complementary,

resource bases often work cooperatively together under a variety of arrangements so that their resources are used more efficiently. These multi-household production units cut across target groups. Behnke and Kervin contend that typification of farmers directs research to formulate recommendations which will inevitably bring about a realignment of these cooperative arrangements. Benefits to individual farmers might be simply a redistribution of productive capacity within the community with no net increase in prosperity. While this is a valid criticism, the need to stratify for research purposes is still important and necessary. The challenge for researchers is to incorporate these findings into the stratification process. An

attempt to do this is made in this study.

Some are skeptical of the recommendation domain approach from an applied perspective. Zandstra (from Shaner, 1983, p. 165), for instance, has difficulty with the concept because the delineation of

a domain depends upon the nature of a future recommendation and this cannot be fully known until the research has been carried out. CIMMYT and others, recognizing this point, identify preliminary recommendation domains and then refine them if necessary as research progresses (Byerlee, Collinson, et al., 1980). Whether or not researchers actually stratify farmers into homogeneous units, they must identify a target group for their research. Due to the critical nature of this identification to the success of research, ability to accurately identify homogeneous subgroups is crucial. Special methodologies, approaches, or guideline criteria need to be developed for the selection of target groups (TAC Review, 1978). This study is a first attempt toward this need.

1.4 Emphasis on the Economic, Social , and Cultural Aspects

In the conduct of the research for this paper, two assertions
are made. These assertions cannot conclusively be proved correct or incorrect, but they make intuitive sense, and logical arguments are developed and evidence is presented to support them.

One recent way to stratify farmers into target groups is on the basis of similar farming systems. (More will be presented on this in Chapter 2.) This would imply that factors which differentiate farming systems are the appropriate factors to be considered when delineating recommendation domains. The goal of successful research, though, is adoption of the recommended technologies by the clients. Factors which affect farmers' decisions regarding technology are the factors, then, which differentiate farmers for the purposes of research (Hart, 1983). They may not be the same factors as those which differentiate

farming systems. Gladwin (1976) maintains that only a study of farmers' decisions on adoption can pinpoint the critical factors or barriers to adoption. Thus, first assertion presented here is: The critical factors affecting farmers' decisions concerning adoption of

agricultural technology are the factors which should he considered when stratifying farmers into recommendation domains.

While it is true that technical elements (environmental and

biological) are important and determine what is potentially feasible

for the farming system, how that system actually evolves is detennined by the human element (Gilbert, et al., 1980). This is the basis for the second assertion presented here: Socioeconomic and cultural

factors are as important as technical factors, and in some situations, more important, to farmers' adoption decisions and, therefore, delineation of recommendation domains.

The following hypothesis is offered, both as an end to wfiich this study is directed and as the rationale for undertaking this

study in the first place. This hypothesis is: that incorporating socioeconomic and cultural factors into farmer stratification in Fanning Systems Research increases the likelihood that research

recommendations will be successfully adopted by client farmers. No empirical test of this hypothesis is possible here. However, presentation of evidence from others' work and logical arguments should establish its credibility.

The purpose here is not to diminish the importance of the technical element in delineating recommendation domains, but rather to enhance the importance of the human element which has traditionally

been slighted. Little research has been conducted on how these socioeconomic factors combine with what is ecologically feasible (Hart, 1983). Only recently have economists and other social scientists been included as full partners on research teams. Thus, the socioeconomic aspects of identifying recommendation domains are emphasized in this study because 1) they are extremely important to the correct identification of target groups and 2) they have until recently been neglected and research on their importance has generally been lacking.

1.5 Proposed Methodology

As indicated earlier one of the purposes of this study is to review and codify the various methods of stratifying farmers into homogeneous subgroups. To this end Chapter 2 reviews the works of

numerous practitioners of FSR and the procedures employed by a number of agricultural research centers practicing farming systems research. Particular attention is given to the extent that socioeconomic criteria

are utilized and to wbich factors are most commonly considered. Often an FSR project will not specifically stratify farmers, but

will, by necessity, identify representative farmers or target farners to participate in the research. This purposive identification of farmers is treated in the same manner. Comments and questions are raised concerning aspects of the different procedures reviewed.

Most of the literature reviewed in Chapter 2 has been gathered by the various professionals at Colorado State University who are or have been actively involved with the development of Faming Systems

Research methods. Other works were borrowed frorn the Faming Systems

Research Library at Kansas State University and from professors and international students at Colorado State University who specialize in small farm development.

In order to accomplish the second purpose of this study (to
identify those socioeconomic factors which should be considered when

delineating recommendation domains), and in accordance with my first assertion, socioeconomic factors are identified which influence farmers' decisions regarding agricultural technologies. Identification of these factors is made in Chapter 3 through a review of selected
literature on econanics and technological change and on technological change in "traditional" societies. Included in this review are several other reviews of constraints to technology adoption, and reports on cases of successful and unsuccessful introduction of agricultural technology. Particular attention is paid to those factors which appear to explain differences in adoption patterns among farmers.
A framework for famer stratification, which is the ultimate objective of this study, is developed in Chapter 4. The relative

importance accorded to various socioeconomic factors in Chapter 3, related to methods and reasoning for stratification presented in Chapter 2, form the basis for this proposed stratification procedure. In development of this procedure, consideration is given not only to influence on adoption decisions, but also to practicality and usefulness in the field. The viability of the proposed procedure is tested with a case illustration using actual data fran Lesotho.

The multitude of socioeconomic factors which may potentially affect adoption decisions can result in the inclusion of too many factors, rendering the resulting stratification framework virtually useless in practical research situations. And interaction among various factors can further complicate this framework. The definition of a recommendation domain and the accompanying research could become so site specific that it would be impossible to extrapolate the results to other areas, thereby negating one of the justifications for identifying recommendation domains in the first place. Navarro (in Gilbert, et al., 1980) commented that farming systems may be sufficiently diverse as to "defy meaningful or at least operational generalization." On the other hand, to omit potentially critical
factors for the sake of simplicity could result in improper identification of recommendation domains, something which has happened all

too frequently in the past.

Obviously it is difficult to d develop a stratification framework which is universally applicable given the diverse nature of farming systems throughout the world. At this time, though, any clarification of the process of stratifying farmers into homogeneous subgroups will be an improvement over the general disarray that currently exists. As Shaner stated in his paper presented at the Second Annual Farming Systems Symposium at Kansas State University, "alternative approaches to stratification need to be refined and taught to many so that this task is not left to a few "renaissance men" who have a gift for identifying the relevant conditions for effective research" (Shaner, 1983, p. 163).


2.1 Classifying Farming Systems

Agricultural researchers have realized that in order to initiate change in the farming sector of LICs they must gain a thorough understanding of the complex farming systems which predominate there. This realization has led to numerous attempts to describe and classify fanning systems in the tropics. As we learned, we began to understand just how complex and diverse these systems are. We realized that "peasant" farmers in the non-industrialized world act in an economically rational manner after all.

T. W. Schultz, in his well-known work, Transforming Traditional Agriculture (1964), was among the first to point out that small traditional farmers were efficient in allocating their meager resources. Development experts began to realize that small farm households make complex decisions in allocating their various resources to satisfy a variety of objectives. An understanding of these small farm "subsistence" economies was sought and reported on by Wharton, Mosher, Ruttan, and others.

One study which describes and classifies tropical fanning systems is Hans Ruthenberg's Farming Systems in the Tropics (1976). According to natural,, economic, and socio-institutional criteria, he defined three broad types of systems: collecting systems, cultivation systems,

and grazi ng systems. Ruthenberg excluded collecting systems as being economically insignificant. He then defined six cultivation systems: a) shifting cultivation, b) fallow, c) systems with regulated ley 4 farming, d) systems with permanent upland cultivation, e) systems

with arable irrigation farming, and f) systems with perennial crops; and three grazing systems: a) total nomadism, b) semi-nomadism, and c) ranching. Other types of grazing systems which combine with cultivation were described as part of the relevant cultivation systems. Among the criteria considered by Ruthenburg in this classification scheme were type of rotation (the long-tem alternation between

various types of land use), intensity of rotation, water supply, cropping patterns and animal activities, implements used for cultivation, and degree of commercialization.

Harwood (1979) took a different approach to classifying farming systems based on their stages of development. On a continuum fran least to most developed he identified hunting and gathering systems,

subsistence-level crop and animal husbandry systems where less than ten percent of the output is marketed, early consumer systems that

market ten to thirty percent of their output, primary mechanization systems where mechanical power is generally used only for tillage,

and secondary mechanization systems wfiere mechanical power is used for virtually all operations.

4 Ley is used wfierever several years of arable cropping are followed by several years of grass and legumes utilized for livestock production. Regulated ley systems are often characterized by individual grazing, fencing, pasture management, and rotational use of grassland. Seeding, hay or silage production, and fertilizer application indicate intensive types. Unregulated ley systems are more akin to short-term fallow systems. (Rutenberg, 1976)

Neither of these classification schemes is precise enough for

most research purposes. Significant variability may be found within these various systems resulting from differing technical and human

circumstances. Farming systems which appear to be the same may in fact be quite different in very important ways. In an area of seemingly similar farming systems, for example, Hildebrand (1979b) found sufficient variation so that technology generated for one group could not be transferred to other groups. For effective intervention, farming systems have to be further defined on the basis of similar


Harrington (1980b) suggests at least two common alternatives for grouping farming systems into relatively homogeneous units. The first is on the basis of agro-climatic and socioeconomic factors that explain important variations in farming systems. The second is on the basis of the current farming system, itself, which is a response to determining conditions. In either case, the various criteria employed in stratification are of concern in this chapter.
Farmers may vary geographically and along hierarchical lines.

-Geographically, they vary in natural factors such as climate, soils, topography, etc.; this is the technical element. Hierarchical variation is from differences in the human element, which can be either exogenous (out of the farmer's control) or endogenous (the farmer has at least partial control) (Norman, 1983b). The human element includes economic and institutional factors as well as factors which

are social or historical in nature, such as food preferences, customs, current technology level, and tenure arrangements (Government of

Zambia, 1979). Of course, there is substantial interaction between technical and human factors.

A wide variety of factors have been used as a basis for stratification at various times. Initially, the technical element was stressed as critical to grouping, often to the exclusion of the-human element. More recently, the human element has gained in prominence, and some suggest that is is more important than the technical element. This review of stratification will focus on the factors variously employed by different strategies. Those that emphasize the technical element are considered first, followed by those focusing more on the human element.

2.2 Stratification Emphasizing Technical Criteria

At the outset it is important to note that the stratification

strategy employed depends somewhat on the orientation of the project or organization. While some of the programs reviewed here are not truly FSR (as defined earlier), they do employ on-fam research techniques and ultimately consider the impact of recommended technologies on the whole farming system. For this reason they are often regarded as farming systems research programs.

Some programs have a mandate that covers a geographically and ecologically diverse area. A few of the International Agricultural Research Centers (IARCs) have such mandates. The International Institute for Tropical Agriculture (IITA) in Nigeria, for example, is charged with developing technologies appropriate for a wide range of tropical farming systems. IITA researchers develop preliminary technologies for specific agro-climatic zones which can be adapted by

the farmers to their more specific farming systems. IITA's rationale for this approach is, given that research resources are inadequate to

cover all systems, the more location specific the research is, the higher are the opportunity costs of neglecting those systems not being researched (Menz and Knipscheer, 1980).

Consequently, IITA identifies the main types of farming systems in its coverage area primarily on the basis of environmental and biological factors. General socioeconomic criteria of small farm systems are considered when planning research, but are not used to

differentiate farming systems.

Similar strategies are employed at the International Crops
Research Institute for the Semi-Arid Tropics (ICRISAT) in India and the International Center for Agricultural Research in the Dry Areas (ICARDA) in Lebanon. Researchers at ICARDA determined that farming systems changed across rainfall gradients and they used this approach to target research on barley production in Syria (Nygaard, 1983).
At ICRISAT, researchers contend that in the semi-arid tropics

water is the most important constraining element. Hence, they use a watershed-based farming systems approach (Krantz, 1981). Apart from

water, soil order is the major differentiating factor due to the different hydrological properties of the two prominent soil orders in the semi-arid tropics (SAT). Farm size and farm resources were also

considered when planning improved soil and water management systems (Krantz, 1981).

Other FSR Programs have a mandate to research one or several crops. As such, their stratification methods may reflect a bias toward the determinants which affect particular cropping systems

rather than the whole farm system. Again, these programs normally consider the effects of recommended changes in the cropping systems upon the whole farm. In fact, some of the pioneering work on FSR methodology has come out of such institutions. The International Rice Research Institute in the Philippines (IRRI) and the International Maize and Wheat Improvement Center in Mexico (CIMMYT) are two IARCs which focus on one or two crops and have strong FSR programs.

Classification of rice systems at IRRI focuses primarily on

technical criteria. A region is divided into rainfall zones by the number of consecutive months of at least 200 mm of rainfall and the

severity of the dry season (Shaner, et al., 1982). Topography, soil texture, and seasonal temperature fluctuations are considered for further delineation. Researchers identify an environmental complex, or a set of sites with similar cropping pattern determinants. They then define a production complex which is a set of sites with similar

cropping pattern performances. If the environmental and production complexes differ, then one or more important determinants has been omitted (Zandstra, Price, et al., 1981). The determinants considered in addition to the above agro-climatic criteria are duration of irrigation, remoteness, and length of time the land has been settled.

IRRI's experience has been that Asian rice farmers are generally willing to participate in researchers' experiments, more so than

farmers in other areas. Consequently, less attention is directed to socioeconomic factors (Shaner, 1983). While this rationale can be

questioned, IRRI has been successful in transferring rice growing technologies.

Several national agriculture research programs also have farming systems research components. At ISRA, Senegal 's agricul tural research agency, researchers have divided the country into ecological regions based on rainfall, irrigation, and soils. These ecological-differences lead to considerable diversity in the faming systems of the country (ISRA, 1979).

At IDIAP, Panama's agricultural research institute, researchers in the Caisan Program determined that farmers within the target area faced similar agroclimatic characteristics, although one locale had

serious market access difficulties (Arauz and Martinez, 1983). This led to a very different pattern of input use, so that two separate recommendation domains were identified. Limited resources forced the research to be concentrated on the more accessible domain. Often researchers, faced with limited budgets, choose those target farmers whose circumstances are amenable to directed intervention, resulting in a more "successful" program. Unfortunately, those excluded from research by this criterion are invariably the poorest, often femaleheaded farm families who might be less likely to adopt the recommendations. Yet, these are the families that desperately need agricultural assistance.

In the Indonesian Cropping Systems Research Program (CRIA),
target areas are selected for policy reasons, for the potential for successful research, and for similarity in soils and climates (Effendi and McIntosh, 1983). Subdistricts are then selected within the target area primarily according to land use types (rainfed, upland, perennial crops, etc.). Market accessibility and infrastructure

development considered influential to the cropping pattern are also taken into account (McIntosh, 1981). Villages identified for research were selected as being most representative or typical of the subdistrict. This was accomplished using a "data matrix" where technical

and economic characteristics form the columns and villages form the rows. The mean value of each characteristic was then subtracted from the respective values associated with each village, giving the deviation from the mean for each characteristic for each village. The villages were ranked for each characteristic, the smallest deviation assigned "1" and so on. These ranks were then summed and the village with the smallest rank sum was considered most representative of the subdistrict (McIntosh, 1981).

A similar method of identifying the most representative village was used by ICRISAT in their village level studies (Jodha, et al., 1977). Using the details of forty characteristics, villages were selected to represent the typical characteristics of the Taluka (sub-division). It should be noted here that at both ICRISAT and CRIA, a number of factors, many of them socioeconomic, were used to identify representative villages. However, stratification into units of commonality took place when identifying subdistricts, and this was done using primarily agro-climatic details. Identifying representative villages simply describes the "most typical" farming system in
the subdistrict.

In the Caqueza Project in Colombia, three zones were delineated by altitude -- high, middle, and low -- with three distinct farming systems. Larger farmers were excluded from this delineation since

they were not the focus of the project (Zandstra, et al., 1976). As with most of the other programs reviewed thus far, a number of other
criteria, mostly socioeconomic, were considered when planning the research and recommending technologies to be tested. This brings up a crucial question: If critical socioeconomic factors are going to be identified, and they must be for successful technology transfer, why not include them in the delineation of recommendation domains so that a more precise definition of the client target group can be obtained? The next section reviews those stratification strategies which incorporate important socioeconomic and cultural elements.

2.3 Stratification Emphasizing Human Criteria

As more socioeconomic variables are included in the stratification equation, the more formalized the procedure becomes. Instead of being divided into physical areas of commonality, farmers are divided into groups or strata of commonality. These strata are not likely to be contiguous geographically. In the Gambian Mixed Fanning and Resource Management Project, ten different strata were identified for analysis on the basis of the pattern of livestock ownership. Not only

were type and size of herd considered, but how the cattle were herded was also considered (the Gambia Project, 1983). Obviously, any

number of these strata could be represented in one area.

Many similarities exist among a number of the stratification

strategies reviewed in the next paragraphs. In most, background and secondary data were reviewed and preliminary recommendation domains delineated. This was usually followed by an informal or exploratory survey and a refinement of domain boundaries. Sometimes a formal,

verification survey was administered and recommendation domains were further refined if necessary. The following strategies considered a wide variety of factors wfien grouping, many of which are socioeconomic and cultural. And most of them incorporated some form of stratification into their methodologies.

At the Agricultural Science and Technology Institute (ICTA) in Guatemala, Peter Hildebrand and his colleagues-have developed an informal survey technique known as Sondeo (from the Spanish verb, sondear, which means to sound out). The Sondeo, or rapid reconnaissance survey, is carried out over a target area by multidisciplinary teams and is designed to gather as much information on farmers' circumstances as possible in a very short time (usually one to two weeks) (Hildebrand, 1979a). This is accomplished through informal

interviews with farmers coupled with regular team meetings to discuss various findings.

From the information gathered in the Sondeo, the predominant

cropping systems used by potential target farmers were selected for analysis and later the areas in which these systems are important were determined. The focus is on the cropping system since it is the

cropping system that ICTA will be modifying with new/improved technologies. It is assumed that environment and circumstances are similar if the same crops are grown in approximately the same manner. The premise is that:

.all farmers who presently use it (a particular cropping system) have made similar adjustments to a set of restrictions which they all face, and since they all made the same
adjustments, they must all be facing the same set of agrosocioeconomic conditions (Hildebrand, 1979a, p. 3).

A rapid reconnaissance survey is also employed by researchers in the Cropping Systems Research Project at CATIE (Tropical Agricultural

Research and Training Center) in Costa Rica. Relatively homogeneous units are defined from information on climate, soil, farm resources, etc. gathered in the Sondeo (Navarro, 1983).

Researchers at the International Potato Center (CIP) in Peru first delimit major agro-climatic zones based on all the natural factors which affect potato production. Different types of potato

farmers are then defined on the basis of the major econom ic characteristics of their farms (Cortbaoui, 1981; CIP, 1978). When identifying technological alternatives, "the socioeconomic knowledge of the

area will help discard unrealistic non-viable solutions" (Cortbaoui, 1981, p. 4).

As an aid to development planning, Eckert (1982) identified

target groups of farmers in Lesotho according to the resources under their control. His stratification was based on productive assets, which include per capita land area, cattle ownership, and capital equipment, and off-farm incomes. His reasoning was that farmers with different quantities and types of resources require different development directions, and, hence, different research strategies.

The Economics Program at CIMMYT has made considerable progress

in formalizing the "recommendation domain approach" to farming systems research. The delineation of recommendation domains is an integral part of CIMMYT's approach. Initial definition of domains is done as farmers' circumstances are analyzed using secondary data. As the

domains are refined after the exploratory survey, major variations in

farmers' practices are related to the circumstances which are hypothesized to influence a particular practice. After the formal survey, recommendation domains are further refined by testing to see if the hypothesized critical circumstance actually does affect the farmer's

management practices. The boundary of a domain should be at the point where a changing characteristic, (eg. farm size) results in different management practices (Byerlee, Collinson, et al., 1980).

CIMMYT researchers caution that it is not necessary to delimit domains too precisely. Recommendations should be general guidelines to which the farmer can adjust according to his own circumstances.

They do note, however, that more domains will be needed when farmers' circumstances exhibit greater variation (Byerlee, Collinson, et al., 1980).

The CIMMYT approach has been used in several projects in Latin America and Africa. In one study in Ecuador, the farming environment was characterized in terms of information needed for agricultural policy. In this case the area in maize, percent of total cropland in maize, average farm size, and maize yields were considered important (Winkleman and Moscardi, 1981).

In another Ecuadorean example three sets of farmers were identified based on natural circumstances: insect patterns and access to irrigation (insect patterns were related to altitude). In this study, it was determined that differences in economic circumstances

were slight for virtually all farmers. Hence, no recommendation domains were defined on the basis of socioeconomic criteria. The

excluded farmers in each set were few in number and small in the

percent of total area given over to maize (Winklernan and Moscardi, 1981).

In a study on maize production in the Peruvian Andes, thirteen

recommendation domains were selected on the basis of altitude, whether or not irrigation was used, farm size, whether there was significant marketing of output or not, and in which of three geographic regions the fanner resided (Benjamin, 1980). Certain fanning systems in a region were omitted from further study because they were insignificant.

In one barley producing area, four recommendation domains were defined according to three factors: rainfall, farm size, and intercropping with another crop (restricting machinery use). These factors

were determined to cause the major differences in land preparation, seeding, varieties, disposal, and input use that were found across the four domains (Byerlee, Collinson, et al., 1980).

In CIMrIYT's East Africa Bureau, Michael Collinson and others

have done considerable work with recommendation domains and have made real efforts to incorporate socioeconomic circumstances into the

stratification process. Included in the exploratory survey were questions concerning a variety of factors relating to the farming enterprises, food preferences,, output variability and range, main production methods for each activity, and resource variability (Collinson, 1982). From this information the following were derived or identified: 1) labor allocation constraints, 2) resource constraints, 3) farmers' priorities and decision criteria, 4) farmers' management strategies, 5) potential points of leverage in the farming system, and 6) approximate levels of return to present cash outlays. Among

other things, this information was used when delimiting recommendation domains.

As a basis for initial grouping of farmers, Collinson uses

variations in the current farming system. His rationale for this is twofold: (Goverment of Zambia, 1979, p. 2)

1. The farming system is a manifestation of a weighted interaction of natural, economic, historical and institutional factors influencing farmers' decisions. It thus reflects
the balance of those factors important in identifying
homogeneous famer populations.

2. The existing farming system is the starting point for
development, the base on which productivity improvements
have to grafted.

Using the current farming system also provides a low cost method for identifying recommendation domains initially; the domains will be . revised as the diagnostic sequence is implemented (Collinson, 1982).

In deriving recommendation domains for Central Province, Zambia,

Collinson made some important observations (Goverment of Zambia, 1979). The key step is identifying the sources of variation which are critical in dictating resource allocations in the farming systems. These key variables vary from, area to area. Secondly, it is important to identify the evolutionary sequence in the development of farming systems. The three keys to system development in the climatically homogeneous Central Province have been infrastructure, demand for maize, and power source. Lastly, Collinson suggests that recommendation domains be compatible with existing administrative divisions in order to fit within existing service channels (Government of Zambia, 1979).

Throughout Central Province, Zambia, it was found that farmers faced relatively homogeneous agro-climatic circumstances. Five

socioeconomic parameters were found to be particularly important in accounting for variations in the types of farming: 1) power source

(closely correlated to cattle ownership and cultivated area), 2) major starch staple, 3) main cash source, 4) degree of adoption of two new

cash crops -- cotton and sunflower, and 5) degree of use of purchased inputs (Government of Zambia, 1979). In the final delineation of

recommendation domains, large-scale commercial farmers (greater than 40 ha) were excluded, emergent farmers (10 to 40 ha) throughout the
province comprised one domain, and traditional farmers (rarely more than 5 ha) were divi ded into six domains based on location and differences in the above five parameters (Government of Zambia, 1979). (See Appendix.)

While working with CIMMYT East Africa, Franzel (1981) writes
that it is often difficult to decide which differences between farmer groups are important enough to warrant labelling them as separate recommendation domains. Certainly, the researchers own judgment comes into play. He contends, though, that in most circumstances, "the differences between target groups in an area are readily recognizable" (p. 25).

David Norman and the Kansas State University team in Botswana

essentially followed the CIMMYT strategy for stratification. Initial typification was based on the existing farming systems (KSU, 1981). It was determined that all small farmers have one overriding problem, timeliness of operations, but the solutions will vary according to the resource base of each farmer (Norman, 1983a). Five particularly

important sources of variation were identified. They were 1) access

to draft power, including ownership and number of cattle, donkeys, and tractors, and the need to hire and exchange labor, 2) major food staples, 3) degree of commercialization, 4) size of crop area, and 5) household characteristics, including male- or female-headed, and condition of equipment and tools (KSU, 1981).

The Kansas State team eventually identified three separate
recommendation domains: 1) wealthy farms with a livestock emphasis,

2) marginal mixed crop and livestock farms, and 3) submarginal mixed farms with a cropping emphasis (KSU, 1981). Power source and size of
holding were regarded as the most important sources of variation while the major food staple was found to vary regionally, not across domains.
Another stratification of rural households in Botswana was done by J.B. Opschoor (cited in KSU, 1981). He defined three groups according to the resource base of the farmer and the farmer's ability to survive in good, normal, and bad years. His purpose was to show that access to resources and the impact of policy-making affect farmers' long-term prospects differently.

In a more pedagogical approach, Hart (1980) proposed a method to initial farming systems characterization based on hierarchical agricultural systems. Like in the Sondeo, multidisciplinary teams were sent out to first describe and diagram the three sectors of the regional system -- the primary sector which extracts or produces raw materials, the secondary sector which processes the output from the primary sector, and the tertiary sector which involves services rather than transformation of goods -- and the flows of money,

materials, energy, and information among them. Inconsistencies among sector diagrams were resolved and a regional diagram was drawn.

Focus of the team then shifted to the primary sector and the

characterization of the farming systems. These were diagrammed, with special attention given to the agroecosystems found on the farm. These farm systems studies are essentially descriptions of the different combinations of agricultural components listed in the primary sector study. Emphasis was placed on qualitative description, which then can be used to identify the phenomena that merit quantitative studies. As with the Sondeo, this systems approach to characterization is designed to be done in the shortest possible time (Hart, 1980).

Employing Hart's approach in La Esperanza, Honduras, a study group of graduate students identified five farm system types: 1) subsistence farms with less than 0.5 ha of potatoes, 2) small commercial farms with 0.5 to 4.0 ha of potatoes, 3) large commercial farms with more than 4.0 ha of potatoes, 4) cattle farms, and 5) fruit farms (Hart, 1980).
Hart contends that classification of farm systems should be according to those factors which affect farmers' decisions with respect to technology (Hart, 1983). He identified six determinants of farmers' design and control decisions. They were 1) the ecological environment, 2) the socioeconomic environment, 3) agricultural resources, 4) household-socioeconomic objectives, 5) flows of material and energy among agroecosystems, and 6) the particular agroecosystems of concern. Using these determinants as criteria, the limits or

boundaries of each type of farm system were defined based on the

points where the level of a determinant triggers a decision (Vart, 1983).

A final approach to stratification is presented, one with

definite emphasis on socioeconomic criteria. Carol Kervin in 1979 typified female-headed rural households in Botswana based on five variables (cited in KSU, 1981). They were 1) cattle ownership,

2) employment status, 3) marital status, 4) availability of household or extended family labor, and 5) remittances sent into the household from outside the community. Using these criteria, Kervin identified six different strata according to the household's dependence on

agriculture, ranging from the high dependency of owners of large cattle herds and arable land to the independency of the wage-employed and households with high outside remittances.

2.4 Summary of Stratification Procedures

It is tempting to evaluate various projects and programs on their stratification methods. Far too little information has been collected, however, to make a fair evaluation. And evaluation is not the intent of this study; rather the purpose is to identify those factors crucial to farmer stratification and clarify procedures for inclusion of these factors in the stratification process. Hence, the various procedures reviewed above are not compared.

This summary, then, is of the factors, and methods, identified and the frequency they have been employed. Physical and climatic factors are not included. Socioeconomic factors thus far identified

are divided into four groups: 1) household resource base, 2) cultural

practices, 3) institutional characteristics, and 4) household characteristics. Each is summarized in turn.

2.4.1 Household Resource Base. Socioeconomic factors most frequently used as criteria in farmer stratification are the resources of the household. These include physical farm resources, labor, cash sources, and resource flows. Of the physical resources,

farm size was most frequently considered, though related variables such-as cultivated areas of particular crops and percent of the total area cultivated in a certain crop were considered explicitly only by CIMMYT. This no doubt reflects CIMMYT's two crop emphasis. Another physical resource commonly considered is power source and the closely related variable of livestock ownership. This is testimony to the

importance of timeliness in farming operations. Several methods considered the availability of farm implements and their condition.

Surprisingly, only two procedures reviewed considered labor

availability, though I would suspect from its importance that others consider it also -- it just didn't show up in the literature reviewed. Source of cash and off-farm income were considered important to stratification by three of the procedures. Only Hart's systems analysis approach explicitly considered resource flows within the

farming system.

2.4.2 Cultural Practices. The most frequent criterion used to stratify farmers is the particular cropping system employed by the farmer. This is said to be a reflection of the combined agrosocioeconomic factors. While not explicitly detailed, one must assume that cropping system includes such practices as intercropping, relay cropping, rotation and fallow systems, major crops cultivated,

and irrigation practices. In Africa, the major food staple produced is used as a differentiating factor. Emphasis on whether the farming system is primarily crop- or livestock-based is used in several of the procedures, and the cropping emphasis of many of the projects implies consideration of this criterion. Method of cattle herding was considered significant in the Gambia.

2.4.3 Institutional Characteristics. These include access to factor and produce markets, credit, transportation and delivery
systems, communication channels, extension services, and famer organizations. While most strategies consider the institutional

climate generally, only market access was explicitly used as a stratification criterion. Market orientation as a criterion employed

by CIMMYT and Kansas State University in Africa, however, implies that some of the other factors affecting market access (transportation,

extension, etc.) were considered as well. Market orientation includes marketed output, use of purchased inputs, and adoption of cash crops.

2.4.4 Household Characteristics. Household characteristics, as a group, were the most seldom considered of all the factors. Objectives and priorities of the household are conspicuously absent in stratification procedures -- only Hart considered them. Gender, employment status, and presence or absence of the household head (or the chief decision maker regarding the farming system) were considered by the Kansas State team and Kervin in Botswana. Only CIMMYT considered the household's food preferences and only Kervin looked specifically at the degree of household's dependency on agriculture.

Two further comments are necessary here. Although evidence on the use of socioeconomic criteria for fanner stratification in some

programs was not found, nowhere was it stated that they did not use such criteria, and, if questioned they would probably indicate that they do. However, such stratification was not formalized in their research procedures. Second, those agencies which stratify according to current farming or cropping systems give implicit acknowledgement to the multitude of determinants which affect these systems. But
implicit consideration of factors still leaves the stratification process very subjective and open to the intuition of the researcher. It is argued here that in order to make this process more objective, certain critical factors must be explicitly considered in the stratification procedure.
One can conclude from this review that the state of the art of stratification is in a bit of disarray. General agreement does not appear, although the approach employed by CIMMYT seems to be the one with the largest following. This stands to reason since CIMMYT has' developed the most definitive approach to famer stratification for research purposes. Lack of agreement may also indicate that, like farming systems research, stratification is too site specific to be conceptualized generally. The following sections of this thesis attempt to dispel this notion, at least partially, by identifying important factors and developing guidelines for stratification.


3.1 Methodological Considerations

In keeping with the assertions presented earlier, this chapter identifies those socioeconomic factors wfiich are believed to be most

influential to farmers' decisions regarding adoption of agricultural technologies and, thus, which should be considered for farmer stratification. A suggested procedure for incorporating these factors into the stratification process is presented in Chapter 4.

Numerous factors have been hypothesized as influential to farmers' adoption decisions. For purposes of stratification, only those factors which may potentially result in differential adoption among farmers need to be considered. Decisions of the field team as to

what constitutes a difference or how much variation is significant are necessarily subjective and depend 6n the particular situation. Therefore, the factors included here are those which should be considered when delineating recommendation domains; however, they will

not all be important in every situation. The -discussion also includes reasons why certain other factors are not as important for stratification. The included factors are summarized in a checklist at the end of this chapter.

I have used influence on adoption decisions as the basis for

inclusion of a factor here. However, assuming that the ultimate goal of small fanner development is improved welfare of the farm family

and rural society, technological solutions are not necessarily the answer. Inequities among different rural classes may actually be

exacerbated by certain technologies (Behnke and Kervin, 1983; Ryan and Binswanger, 1979). The plight of certain household members (i.e., women and children) may also be worsened with the introduction of certain types of technology. These issues must be kept in mind

when stratifying farm families. Their importance is reflected, I think, in the emphasis on socio-cultural issues in this discussion.

Adoption of a recommended technology requires that the recommendation be technically correct, economically justified, and socially and culturally acceptable. The technical requirement, though not included in this study must not be construed as unimportant. Perrin and Winklemann (1976) found agroclimatic and topographic factors to be most important in explaining differential adoption rates among fame rs. Low (1982) in Swaziland found that adoption of hydrid maize was positively related to suitability of an area for maize production. Others similarly agree on the importance of the technical element.

Widespread agreement also exists on the importance of economics

to adoption; indeed, many consider it to be of primary importance (Foster, 1973; Wilson, 1977; Gladwin, 1976; Beal and Sibley, 1967;

and others). Gladwin found non-profitability to be the major reason why Mexican farmers decided not to fertilize twice as was recommended.

Beal and Sibley cited research supporting the importance of economic motivation to adoption of agricultural technology. Interestingly,

though, in their empirical study of adoption by Guatemalan Indians, the data did not support a positive relationship between adoption and economic motivation. They attribute these findings in part to the

alien nature of economic gain and accumulation to the Indian culture, supporting the importance of the socio-cultural requirement. They

also suggest that perhaps the questions were not properly presented, and they cite the need for further study before the theory supporting the importance of economic motivation is rejected.

One possible conclusion from this is that economic motivation is not as important in very traditional societies. However, the concept of economic gain is widespread enough that its importance cannot be disregarded. Thus, the general conclusion here is that economics is significantly influential to farmers' adoption decisions; this is

supported overwhelmingly in the literature.

A certain amount of caution should be exercised when interpreting evidence on the relationship between various factors and technology adoption. Few studies provide information on the effectiveness of
on-fam use of improved technology (Schutjer and Van Der Veen, 1976). When a technology is reported as being adopted, the degree of adoption and the extent to wbich farmers apply the new practice is usually unknown. Yet, such information would seem important in determining
"successful" adoption and adoption differences.

Remember that the rationale for farmer stratification is to
identify groups of fam families for *om we can make more or less the same recommendations. Although each farming system is unique, research cannot be farm specific. Thus, research recommendations need to be sufficiently general so as to be applicable to a group of

similar farmers. Farmers can then adapt the recommendation to their particular circumstances. It would not be cost effective to delineate recommendation domains too precisely. Decisions must be made on how

much variation warrants a separate domain. But, this determination cannot be completely scientific: certain factors may be -more important than others in a particular instance; some systems are more flexible than others; or a certain parameter may be very rigid for one family, yet easily changed for another. Thus, much of stratification remains an art which a set of criteria cannot replace.

Finally, to add some order to the discussion, I have chosen to categorize the various socioeconomic factors according to the way they might be identified by field personnel. First I consider the characteristics of the community in which the farmer operates. Next, I look at the factors of the fam as a fi m, and finally, I consider the characteristics of the household not specific to the farming

operation, although the close relationship between these last two make this distinction somewhat arbitrary. In fact, the interrelatedness of the fam to the household and both these systems to the larger community system make any categorization scheme difficult.

3.2 Community Environment

The famer must operate within a larger community environment. This includes not only the agro-ecological environment, but institutional, demographic, and sociocultural environments as well.

It is incorrect to assume that the institutional environment
poses an equal constraint to all farmers in an area and therefore is unimportant to stratification. Access to institutions may vary within an area for physical reasons (e.g., distance from road), because of resources differences (e.g., ownership of an ox-cart or

vehicle), and due to farmers' perceptions.

Institutions are significant, man-made elements (such as practices, regulations, and organizations) in a culture which guide behavior and which center on fundamental needs, activities, or values. As used here, institution refers primarily to those elements bearing directly on agricultural activity, such as markets, transportation, credit, and communication systems. Land tenure is considered with land resources in section 3.3.

3.2.1 Market access. Access to product, input, labor, and food markets depends on physical factors and psychological factors. Of

concern to stratification is whether different farmers have differential access to these markets. Although it is not possible from the literature to document the extent or impact of differential market access on rates of adoption, general agreement exists that such differential

access is a major barrier to adoption by many farmers (Schutjer and Van Der Veen, 1976). Therefore, significant differences in market access may require different recommendation domains.

Work patterns, landholding and cropping patterns, choice of crop,, use of purchased inputs, and cultivation methods are all influenced by market access (Norman, et al., 1982). Prices paid and received by farmers may vary due to differences in market access. Local factor markets make it possible for farmers to better allocate

their resources, particularly with regard to labor and power source, and they serve to mitigate unequal factor endowments (Ryan and Binswanger, 1979). Markets are needed to supply food and consumer goods. And, of course, adequate market access is required before farmers will produce beyond their own needs.

Physical access to markets depends on existence of markets,

infrastructure development in the area, and farTners' own resources. Assuming markets do exist, an obvious way to ascertain how physically accessible they are to the farmer is to look at local roads and transportation, their condition particularly during the wet months,

and the distance from the farm to the road and to market centers. Also important is whether the famer owns a vehicle, ox-cart, or other means of transporting supplies and produce, or wfiether such
transport is locally available.

If the farmer has seemingly good access to markets yet has low market activity, then maybe his "psychological" access is bad. He

may perceive that markets are inaccessible to him, or he may believe that he is treated unfairly (Beal and Sibley, 1967). This could be

due to a number of factors. Different farmers may face different costs and remunerations depending on who they deal with, whether they

belong to a cooperative or other farmer organization, existence of quantity discounts and bonuses, and their ethnic or class origin.
Different farmers may face different market transactions costs, or they may have different perceptions of their market accessibility.
Finally, farmers' differing goals and motivations may affect whether or not they wish to produce for market sale.

3.2.2 Access to other institutions. In addition to market access, access to extension and credit are frequently considered essential to adoption of recommended technologies. With both, however, evidence supporting this relationship is at best mixed.

Access to market information is essential to farmer participation in the market. Access to other information is necessary as
well, particularly information on new technologies and methods. For instance, Gladwin (1976) found that lack of knowledge on the recommendation was most important in explaining why Mexican farmers decided not to increase plant populations as was recommended. Ability to

adopt new practices and participate effectively in the market place depends on timely information and knowledge. Differences in access

to such information can result in differential adoption of technology. If these differences are not easily corrected, then separate recommendation domains may be required.

Information to farmers can come via different sources. As a source of information on new technologies, extension is believed to be important. No evidence was found, however, that indicates that extension is positively related to adoption rates. In an AID study of 51 extension agencies in 1,560 farming communities in Latin Anerica, it was concluded that "the assistance of the extension services was neither sufficient or necessary for modernization," (cited in Hatch, 1976, p. 2). In the Philippines, Barlow, et al. (1983) found that once information on new varieties was known, the presence of extension or research personnel was not required for further adoption. Their presence may, however, accelerate adoption.

Past use of recommended practices is one key to the adequacy of information sources. Depending on the channels of communication in the area, certain factors, such as ownership of a radio and membership

in effective farmer organizations, may clue researchers to the degree of access to information. Also, a farmer's negative attitude toward

goverment or unscientific behavior may indicate inadequate access to information.

Evidence on the importance of credit to technology adoption is also inconclusive. In Plan Puebla in Mexico, Gladwin (1976) found lack of credit to be an important factor in farmers' decisions on fertilizer use. Valdez and Franklin (1979) noted the importance of a

market for renewal of long-term credit, and Norman, et al. (1982) found that savings and credit can help to overcome cash flow problems created by the introduction of new technology. On the other hand, Scobie and Franklin (1977) found that membership in a supervised
credit program, which entailed restrictions on input use, made no significant difference to adoption of technology in Guatemala. Perrin and Winklemann (1976) concluded that, wbile use of credit was found to be significantly related to adoption of new varieties,

credit programs are not necessarily critical to farmer adoption except in the case where new technology is marginally profitable.

Tinnermeier (1983) concludes that while some form of access to capital is important, reliability of credit is more important than cost (interest rates). Informal credit markets are often more reliable than are formal markets. Even access to informal credit may not be a

requirement for adoption. Farmers will often find a way to adopt a technology which is profitable. In the Philippines, introduction of new profitable varieties resulted in the creation of an informal credit market (Barlow, et al., 1983). Schutier and Van Der Veen (1976) concluded that access to institutional credit does not seem to be a prerequisite for technology adoption except in the case of lumpy

investments such as tractors and tubewells. They also suggested, however, that more evidence on this relationship is needed to make a definite conclusion.

3.2.3 Ethnic or class differences. Certain social characteristics in an area bear upon farmers' decisions on technology. Foster (1973) identifies several important social barriers to change. These include locus of authority -- roles of village and religious leaders and public acceptance of each leader's authority; group organization formal and informal famer groups, group rivalries, acceptance of innovators, and peer pressure; and social structure -- caste and class barriers and societal configuration autonomyy, hierarchical, central control, etc.).

Different ethnic groups generally have significantly different cultural and social structures resulting in differing attitudes

toward change. Their goals and obligations may differ and they may have different perceptions of the institutions and resources available to them. Different classes or castes often have significantly different resource endowments as well as different perceptions. All lead to potentially different adoption behavior. For stratification purposes the criteria are straightforward: different ethnic or class groups should comprise different recommendations; that is unless the field team determines rather conclusively that groups of differing ethnic background are still very similar in their adoption behavior. Stratifying by ethnic group will account for many of the sociocultural differences which are potentially significant.

One must also consider the historical development of a society, particularly in the past several decades, and the resultant breakdown of social institutions due to technological change. Expansion,

splitting, or combining of ethnic groups influence technological development and resource allocation (Collinson, 1972). Particularly for marginal producers, it is important to know how agricultural production has changed over time and how intense or rapid has been

environmental degradation (Garrett, 1983).

Census and population data are the easiest sources from which to determine if there are different ethnic groups in the area. Local leaders, officials, and merchants can also identify different ethnic and class groups. Obvious variations in housing and compound structure and differences in local lines of authority may also indicate ethnic or class differences. Usually, this information is not difficult to obtain.

3.2.4 Pooulation densities and local employment characteristics.

Increased population densities result in land becoming constraining relative to labor, which necessitates different technological recommendations (Norman, et al., 1982; Ryan and Binswanger, 1979; Spencer and Byerlee, 1976; and Cleave, 1976). It also results in less fallow

land, decreasing soil fertility, and more fragmented farms (Norman, et al., 1982; Collinson, 1972).

Population densities also affect off-farm employment opportunities and supply of local labor, both of which affect local wage

levels. All three combine to influence farmers' management decisions (Norman, et al., 1982). Farmers' decisions to produce a surplus are

affected by the wage they could earn off-fam relative to returns on marketable produce (Low, 1989.). Local wage levels also affect methods of planting, cultivating, harvesting, etc. (Barlow, et al., 1983).

While population densities and local employment characteristics

are the same for a group of farmers in a given locale, they critically affect farmers' adoption. They should be incorporated in the definition of recommendation domains since some domains may include farmers from different areas, and since research results are likely to be
extrapolated to other areas. This information is normally derived from analysis of secondary data and included in the preliminary definition of recommendation domains.

3.2.5 Group interactions. As Behnke and Kervin (1983) pointed out, analyzing homogeneous groups in isolation often masks important interactions among different economic classes. Reciprocal arrangements for labor, tractors, and draft animals are commonplace among

farmers in low income countries. Many farmers depend on these arrangements for timely completion of agricultural and non-agricultural activities. Not only do these arrangements affect their participants' decisions, technological solutions may significantly alter such

arrangements to the detriment of one or more parties. Different farmer groups may also interact in the control and use of communal resources. This is particularly common in much of Africa where

traditional tenure arrangements still prevail.

While the benefits of inter-group arrangements probably appear in other factors used in stratification, the ensuing obligations may not. There, the existence and nature of cooperative arrangements should be considered when stratifying. Behnke and Kervin suggest

using supra-households as the basis for research when such arrangements exist. Unfortunately, this would probably complicate an already difficult research task. The researcher will have to use her 5 training and knowledge of the situation to decide how to stratify farmers who are partners in inter-group arrangements.

3.3 Farm-Firm Characteristics

There is wide agreement that farm resources are very important in explaining differential adoption rates among farmers. Beal and Sibley (1967) found that fam fi m variables explained 25% of the variance in adoption among the group of Guatemalan Indians they

studied. Garrett (1983), Eckert (1982), and others have concluded that different research strategies are required for farmers with different resource levels. The farm resources discussed here are labor, land, capital equipment, and cash. The risk factor associated with a farmer's resources interacting with the institutional and ecological environments and interactions within the farming system
are also discussed in this section.

3.3.1 Labor,. The supply of labor, both from within the household and from outside, relative to the scale of operation is a most
crucial determinant to farmers' decisions (Norman, et al., 1982). The farmer's own labor to land ratio determines the need for outside labor and these combine to constrain the level of year-round agricultural activity to the amount of land which can be worked during

5 The feminine program is used occasionally to reflect and
emphasize the fact that women play a significant role in agriculture in most low income countries.

bottleneck periods (Norman, et al., 1982; Zulberti, et al., 1979). Availability of labor determines whether land-augmenting or laboraugmenting technologies are acceptable, and appropriate solutions depend on correct identification of labor bottlenecks (Barlow, et al., 1983; Spencer and Byerlee, 1976; Morss, et al., 1976). Labor availability is clearly a function of household size, which also determines household food requirements. This influences the agricultural activity of the household. Norman, et al. (1982) found a positive relation between household size and cultivated area and yields.
Type of labor available relative to off-farm employment opportunities is also important in labor-use decisions (Low, 1982; Zulberti, et al., 1979). In Swaziland, where off-farm employment opportunities do exist, Low found that production of subsistence goods was done first by those members with the lowest opportunity costs. In fact, he found that households adopted high yielding varieties of maize in amounts just sufficient for household needs but did not produce for market because labor was better used in wage employment. The returns to wage labor were higher than returns to labor used in commercial production of these new varieties, though the cost of buying maize for own consumption was high enough that labor was used in subsistence production. Consequently, introduction of high yielding varieties of maize did not result in an increase in surplus production as was hoped.

Cleave (1976) calls labor the most complex of all factors because it depends on biological factors (age, sex), technological factors (training) and economic factors (opportunity costs). He also

considers it to be of overwhelming importance to farmers' decisions. The literature supports the conclusion that quantity and type of labor available and its relationship to the labor market are important in explaining differential adoption, though empirical data confi ming this relationship are limited.

As a stratification criteria, labor is rather complex. Laborsurplus households and labor-deficit households might warrant separate
recommendation domains. Farmers with the same labor bottlenecks can be included in one domain, barring other significant differences. And areas with different labor market features (supply and demand) and wage rates may require separate domains. When considering the type of labor available to the farmer, stratification becomes more difficult; specific circumstances will dictate domain boundaries.

Quite possibly, farmers with a supply of skilled labor will react differently to a set of recommendations than will a farmer having primarily unskilled labor. And a household with a large number of children would be different from one composed largely of adults. The
number of female workers relative to male workers might be a differentiating factor as well.

A number of factors influence labor availability. In the community, population densities, seasonal migration patterns, employment opportunities, level of unemployment, and prevailing wage rates all affect the labor market. On the farm, household size and composition, role

distinctions within the household, farm size, returns from surplus production relative to returns from off-fam employment, ability to hire labor, and additional food requirements of hired labor all

influence the farTner's labor decisions.

3.3.2 Land. Given the frequency that farm size is used as a

differentiating factor, it is surprising to find the lack of agreement on its relationship to technology adoption. While some studies have found size or area cultivated positively related to adoption (Beal

and Sibley, 1967; Arauz, and Martinez, 1983), others have found no significant relationship (Barlow, et al., 1983; Ryan and Binswanger, 1979). Schutier and Van Der Veen (1976) concluded that size of land holding is more closely correlated with lumpy, indivisible technologies. However, the introduction of custom work and a wider variety of tractor sizes, and the improvement of markets and other institutional mechanisms have partially overcome this indivisibility problem. Size of holding is often closely correlated with ownership of other resources as well, comnplicating the relationship between farm size and technology adoption. Thus, the influence of farm size on adoption is primarily through intervening variables such as institutional access, wealth, power, and social status; size per se has little correlation to adoption (Schutjer and Van Der Veen, 1976; Perrin and Windkemann, 1976). However, economies of size in transactions costs and the

ability to obtain quantity discounts can result in differential adoption rates of even completely divisible technologies, leading

small farmers to sometimes lag in adoption (Perrin and Winklemann, 1976).

Evidence of the relationship of tenancy to adoption is also

mixed. Schutjer and Van Der Veen (1976) conclude that what influence there is, is indirect through variation in access to credit, markets, and technical information. Morss, et al., (1976) found that social control of land use and competing uses for fallow land constrain

adoption, and Scandizzo (1979) noted that landlords and tenants favored different technologies.

Fragmentation of holdings becomes more important in irrigated systems (Ryan and Binswanger, 1979), and with the introduction and

use of farm machinery. Group action required to effectively manage an irrigation system is more diffkult when there are many small

fields. Delivery systems and tractorization programs are less effective when fields are highly fragmented. And the distance that farmers must travel between fields influences their management of those fields.

While its effects on farmers' decisions are primarily through

intervening variables, farm size may reflect one or more other factors
and is easily understood as a basis for stratification. However, farm size alone is usually not sufficient to explain differences

among farmers unless the variation is wide; rather farm size relative to other resources, particularly labor, is the more crucial element. Differing land/labor ratios might be an appropriate criterion for stratification. Differing tenancy arrangements would be a basis for

stratifying farmers if the differences were substantial enough to result in significantly differing adoption decisions. Fragmentation may be significant in explaining differences among farmers, particularly in irrigated areas and in areas with mechanization programs.
3.3.3 Capital equipment. Timing of farming operations is
crucial to successful cultivation, particularly in rain-fed agriculture. Besides labor, the critical variable affecting timing is power source. Adequate power is also necessary to increase the extent or intensity of farming operations. Ownership of or access to

tractors or draft animals, and their condition and health, are important differentiating factors among farming systems (KSU, 1981; Barlow, et al., 1983; Byerlee, et al., 1980; Eckert, 1982; Banta, 1980).

Sometimes variation in power source among farmers is obvious,

other times it is not. Farmers have often been stratified according to the number of draft animals owned. The difficulty is in deciding at wfiat number to separate into another domain: e.g., are five head of cattle significantly different from twenty head as far as power is
concerned? Obviously, a famer with access to a tractor will have different technological capabilities than a farmer with a span of oxen. The difference is not so obvious when comparing a two-wheeled hand tractor to draft animals, or wfien comparing oxen to horses. A farmer with access to good dry season forage may have a decided advantage over one without such access.

When using power source as a basis for stratification, the

crucial question is whether power is available wfien needed and in the quantity needed. Significant variation in source of power among farmers may result from ownership of a tractor or draft animals,

ownership of necessary implements, availability of hired traction and the farmer's ability to hire it, and condition of the tractor and implements and health of draft animals at the time they are needed.

Shared arrangements for draft power and access to communal or government tractor programs are also important considerations.

3.3.4 Cash and income. One would expect that availability of

cash, from whatever source, is necessary for technology adoption. Although Schutier and Van Der Veen (1976) found there was no clear relationship of cash to adoption, several more recent studies have

concluded that cash availability and farmers' willingness to expend cash were clearly constraining to adoption of even profitable technologies (Barlow, et al., 1983; Byerlee, et al., 1980; Zulberti, et al., 1979). Farm income and per capita income were also found to be positively related to adoption (Morss, et al., 1976; Beal and Sibley, 1967). This supports the importance given to off-farm income sources by several of the stratification stragegies reviewed in Chapter 2.

Important for stratification are the farmer's ability to purchase o r hire necessary inputs, his ability to meet financial obligations and make additional investments, and his seasonal cash flow. Factors

which determine whether cash is constraining are the farm family's income, both from farming operations and from off-farm employment, and the family's cash requirement. Cash is required for farming needs and households needs such as additional food, clothing, school fees, and consumer goods. Household needs are dependent upon household size and stage of development as well as kinship and social obligations. Access to borrowing, either formal or informal, is more important for families with very limited resources who often find credit access most difficult. Seasonal cash constraints may be indicated by sale of crops and livestock at harvest and buying food

later at higher prices, working off-fam at a time of labor shortage on the farm, and borrowing short-term at unfavorable rates.

3.3.5 Risk factor. The level and quality of farm resources

interacting with the ecological and institutional environment determine the riskiness of the situation in which the farmer operates. Degree of variability in factors beyond the farmer's control is reflected in

the risk factor, composed of yield risk due to ecological variability and price risk due to market and price level variability (Byerlee, Collinson, et al., 1980; Ryan and Binswanger, 1979; Cleave, 1976). The farmer's resource base determines her ability to absorb risk. It is generally agreed that the risk factor associated with a farmer's environment impinges on adoption of agricultural technology: technologies which increase risk are less likely to be adopted than technologies which decrease risk (Zulberti, et al., 1979; Valdez and Franklin, 1979). However, the risk factor should be reflected in the analysis of the farmer' s technical and institutional environments and resource base. Therefore, it should not be necessary to include it as a factor, for stratification, although the general riskiness of a farmer's environment should be kept in mind. The risk orientation of the farmer is looked at in conjunction with household orientation in

section 3.4.3.

3.3.6 Interactions within the farming system. By looking

strictly at components of a farming system, important interactions can easily be overlooked. FSR takes a holistic view of the farming system which requires an understanding of the dynamic interactions

and relationships within the farming systEm which bear upon farmers' decisions. These interactions include competition for and allocation of scarce resources (input-input relationships), canplementarity among enterprises where the output of one is the input for another (input-output relationships), and the relationships among different enterprises to satisfy the various needs and objectives of the household (output-output relationships) (Byerlee, Collinson, et al., 1980;

Moreno and Saunders, 1978). Banta (1980) suggests that an understanding

of input-input relationships (resource ratios) is particularly important. And it has become widely accepted that relationships between enterprises, especially between crops and livestock, are important to farmers' adoption decisions. There have been many cases where a new crop variety was not acceptable because the residue was not suitable for fodder.

For stratification purposes, significant differences in system relationships which potentially influence adoption decisions should be identified. In more traditional systems, these relationships are more complex and their identification is more important. Differences

may be identified by flow-charting the farming system (Hart, 1980, 1983) and by establishing the disposition of output from each enterprise. Some differences may be accounted for when distinguishing ethnic groups.

3.4 Household Characteristics

The households characteristics discussed in this section, While closely related to farm-firm characteristics, are those which, to be identified, require examination of the household as well as the farming system. They frequently explain why farmers, operating under seemingly similar circumstances, react differently to recommended changes. Yet these household characteristics are often most elusive to the research team and, hence, are seldom considered not only when stratifying farmers but when planning and designing research as well.

6 Household, as used throughout this thesis, refers to a group of people, not necessarily related, who produce and consume as a unit.

3.4.1 Household composition. Compositon of a household includes its size, make-up, and organization. Differences in composition can account for differences in labor availability, in household requirements, and in goals and motivations,-all of which influence farmers' deci sions.

Composition and organization of the household are important to farmers' decisions through delineation of control and responsibility within the household. Roles are clearly defined in subsistence agriculture, usually on the basis of sex and age (Cleave, 1976). Women's contribution to both agricultural production and off-farm income are often considerable. Control of certain resources and the
returns generated therefrom are often divided by sex and are usually independent of each other (Garrett, 1983; Norman, et al., 1982). Clearly, role distinction within the household has a major influence on decisions concerning technologies as well as on effects of technological change (Spencer and Byerlee, 1976). This latter point is

particularly important since the effects of change on women have so often been overlooked; and these effects are often detrimental.

Norman, et al. (1982) noted that whether the household was complex or nuclear affected decisions on technology adoption. A change from complex to nuclear units resulted in less communal and more individual control of fields. The resulting decentralized decision-making created problems in introducing technologies when fields were not controled by the family head. Fresco (1979), Kervin (in KSU, 1981) and others have argued that households headed by women should be a separate domain since they are usually resource poor and

they differ from riale-headed households in decisions regarding technol ogy.

Stage of__development of the household, though hard to ascertain, influences its composition and, thus, its resources, needs, and goals. Fortes (cited in Low, 1982, p. 24) characterizes the domestic development cycle into five stages: establishment, expansion, consolidation, fission, and decline. Households at different stages in their development cycle may be expected to exhibit different production characteristics. Those early in the cycle normally have strong desires to accumulate, emphasizing surplus production, and have high

consumer/worker ratios resulting in high demands on workers and greater intensity of labor input per hectare (Low, 1983). Households

in the middle of their cycle are larger and have a definite resource advantage, while households late in the cycle have less labor available, are often fem.ale-headed, and have little desire to accumulate for the future (Low, 1983). The stage of development, then, can account for differences in household's goals, resources, and employment prospects.

Composition of the household may provide a basis for stratification along several lines. In many societies, particularly in Africa,

a number of households are headed by women, at least in a de facto sense. In these cases, gender would be a basis for stratification.

Complex households might be differentiated from nuclear households. Households in the early stages of development, those in the middle or

consolidation stage, and those in later stages may warrant three different domains. Role distinctions within households, if significantly different, might also fom a basis for stratification.

Identification of differences in household composition will come

out of farmer interviews. Particular attention should be paid to

work patterns within the household for both agricultural and nonagricultural tasks. Roles of various household members in the different activities should be identified. An accurate assessment of this implies that members other than the household head need to be interviewed as well.

3.4.2 Household goals. Decisions made by a farmer relate

household goals and objectives to the specific situation in which the

household operates. While compatibility with farmers' circumstances is necessary for adoption, compatibility with farmers' goals provides the sufficient condition for adoption (Norman, et al., 1982). Gilbert, et al. (1980) refer to goals and motivations as "the motor that drives the entire system" (p. 9).

One view is that the desire for economic gain ultimately outweighs other goals; that food preferences, risk aversion, etc., simply modify the economic goal (Byerlee, et al., 1980; Foster, 1973). Others contend that non-economic motivations are paramount. Collinson (1982), for one, gives the priorities of farmers as 1) social and cultural obligations, 2) reliable supply of preferred foods, 3) cash for additional basic needs, and 4) extra cash. He suggests that this order of priorities is fairly firm, though the weight given to each will vary depending on how close the household is to subsistence.

Others argue that providing for subsistence -- satisfaction of the household's basic food requirement -- is probably the most

important goal of small farm families (Norman, 1983; Norman, et al., 1982; Cleave, 1976). This subsistence need may even lead marginal farmers to value their wage employment more highly than their farming operation, resulting in resources being directed off-farm (Behnke and

Kervin, 1983). Other goals which are influential to farmers, decisions are food preferences, kinship and social obligations, and

desire for social status, power, and prestige (Norman, et al., 1982; Anthonio, 1977; Cleave, 1976).

The important point is not whether economic or non-economic

goals are primary -- they both are important. Rather, the point is that differing goals result in different decisions. When stratifying farmers, identifying differences in the relative weights given to various goals is the key. Of particular concern is the importance attached to non-economic goals. Subsistence needs, security, and

food preferences dictate cropping patterns, choice of variety, planting dates, and storage and marketing strategies. The strength of these goals may vary according to the family's level of subsistence, its stage of development, and its involvement in the market. A high

emphasis placed on food security may be identified by diverse cropping patterns and use of traditional varieties and technologies, particularly on the main staple crop. Substantial household expenditures for school expenses, health care, etc., or a household in its early stages of development probably indicate a greater emphasis on economic gain, particularly if household food requirements have already been met.

3.4.3 Household orientation. The orientation of the household toward market production, toward government, and toward control over nature (scientific orientation) may help explain low market activity and low adoption rates. Orientation reflects and influences household

goals and motivations. Importance of societal norms diminishes as

farmers acquire a more positive market and scientific orientation (Norman, et al., 1982; Cleave, 1976).

Among the most difficult factors to identify are farmers'

attitudes and perceptions, yet they can dramatically influence adoption decisions. As perceived by an outside observer, resources might be present at optimum levels; yet farmers may perceive these resources as inadequate or even non-existent. Beal and Sibley (1967) cited
studies which indicate that adoption is related to farmers perceptions and the results of their own study in Guatemala generally supported this. Foster (1973) argued that differential cross-cultural perceptions are important barriers to change. Farmers' attitudes towards government and institutions, and their attitude toward control over

nature, or scientific orientation, all affect adoption decisions (Foster, 1973; Beal and Sibley, 1967).

The problem is that farmers' attitudes and perceptions are difficult to ascertain. Market orientation, which is more easily determined, can serve as a proxy. Market orientation has been shown to be positively correlated to adoption rates (Norman, et al., 1982; Wilson, 1977; Cleave, 1976; Beal and Sibley, 1967). Thus, the degree of market orientation should be a basis for stratifying farmers.

Several factors can be used to identify a household's market orientation. Historical use of recommended technologies and past participation in development schemes have been found to explain variation in adoption (Barlow, et al., 1983; Beal and Sibley, 1967) and it gives an indication of the degree of market orientation. Extent of cash cropping, use of purchased inputs, number of consumer

goods purchased, food purchases, income, and participation in local organizations are also good indications.

A determinant of market orientation is the household's degree of averseness to risk, or its risk orientation. Risk avoidance is generally concluded to be a constraining factor in adoption of agricultural technologies by small farmers (Norman, et al., 1982; Zulberti, et al., 1979). The closer a household is to a minimum societal standard for survival, the more strongly risk avoidance affects their behavior (Wharton, 1968). The question is whether there is sufficient variation in the risk orientation of small farmers to include it as a

criterion for stratification.

While some studies have reported the degree of risk aversion to be negatively related to technology adoption (Moscardi, 1979 and Beal
and Sibley, 1967, for example), a number of others have concluded that the degree of risk aversion is not responsible for differential

rates of technology adoption (Norman, et al., 1982; Sanders and Dias de Hollanda, 1979; Roumasset, 1979; Schutjer and Van Der Veen, 1976). The argument is that small farmers are at least moderately risk averse, but that variation in their risk.orientation is minimal. Where such variation exists, it does not appear to contribute to adoption differences. And, of course, some differences in risk orientation are accounted for in differences in market orientation.

3.4.4 Other household characteristics. Certain cultural traits of the household influence adoption of agricultural technology. To be accepted, technology must be compatible with norms of modesty, work values, customary motor patterns and body positions, and superstitions and taboos (Foster, 1973). While these may be the cause of

scme variation in adoption decisions, they generally do not vary within ethnic groups and it would probably not be cost effective to ascertain such information for stratification.

3.5 Checklist of Socioeconomic Factors

Theoretical evidence and arguments suggest the existence of certain relationships between different factors and adoption of agricultural technology. These relationships have been reviewed above. However, empirical data supporting the theories are scarce. Drawing on wfiat evidence there is and on the conclusions of others, this chapter has identified a number of socioeconomic factors which

appear to explain differences in small farmers' adoption behavior.

The most important of these factors or characteristics are

summarized in a checklist (Table 1) at the end of this chapter. For each factor, possible criteria for stratification are given and key contributing factors which may explain or help to identify variation in the factor are presented. Inclusion of a factor in this checklist was dependent primarily on its apparent importance in explaining differential adoption among small farmers. Other considerations for inclusion were the extent of influence of a factor on other factors (e.g., household goals), the frequency or likelihood that a factor or characteristic may be overlooked by the field team (e.g., interactions), and the relative ease with which variation in a factor might be identified.

This checklist is intended. to be just that, a checklist. It is not meant to be a blueprint for stratification, nor is a blueprint possible given the diverse nature of small fanning systems around the


world and the site specificity of farming systems research. Rather, the intention of this discussion is to assist researchers in identifying sources of variation among farmers Aich are significant enough to warrant separate recommendation domains. A possible procedure for incorporating these factors into the stratification process is presented in the next chapter. The procedure is then illustrated with the case of Lesotho's lowlands.

Table 1. Socioeconomic Checklist for Farmer Stratification


Factor or Characteristic Criteria for Stratification Key Contributing Factors


Differential access physically and
psychologically to product, factor.
and food markets

Differential access to market
information and information on
new technologies, provided that
inadequate access Is not easily

Different ethnic groups and socioeconomic classes
Existence and nature of reciprocal, cooperative, and competive arrangements among different farmer groups

Distance from roads and markets Infrastructure development, including transportation Market information Farmer's cash and transportation resources Farm size & ethnic differences resulting in different
prices and transactions costs
Farmer's past A current market activity: extent of cash
cropping, use of purchases Inputs, food purchases, and
consumer goods
Membership in local organizations Farmer's perceptions of market access

Past use of recowinded technologies Extension
Membership in local organizations Radio ownership
Farmer's Infornation source behavior Farmer's attitude toward government

Arrangements for sharing and exchanging resources Commnnal work arrangements Control and allocation of common resources

Table 1. Socioeconomic Checklist for Farmer Stratification (cont'd)

Factor or Characteristic Criteria for Stratification Key Contributing Factors


Population densities and unemployment rates
Employment opportunities and wage rates Seasonal emigration patterns Household size, particularly relative to farm size Household composition Cash resources to hire labor Returns from surplus production relative to returns
from off-farm employment
Farmer's perceptions of the local labor market

Livestock or tractor ownership Availability of hired tractor Cash resources to hire traction Shared arrangements for traction power
Condition of machinery and implements Health of animals at the times when they are needed
Income, Including off-farm income Household cash requirements Seasonal receipts and payments Early sale of harvest, off-farm work, and short-tern
borrowing may indicate seasonal cash constraints
Access to capital (credit), particularly for resource-poor
Cropping patterns, intensity, and rotation may indicate
land constraint and degree of fragmentation
Farm area, area under cultivation, area in a particular
Household size and food requirements Tenancy agreements

Labor-surplus or labor-deficit
households;, different labor bottleneck periods; significantly different
types of labor available;, and areas
with different labor market

Differences In timely availability
of adequate power; significant
differences in the type, quantity,
and condition of power source

Differences in farmers' abilities to
purchase inputs and meet financial
obligations, significantly different
cash flow problems; and significant differences in off-farm remittances

Vide differences in farm size.
particularly relative to labor and
other resources;, significantly
different tenancy arrangements;, and
significant differences in degree
of fragmentation, especially in
irrigated systems



Table 1. Socioeconiomic Checklist for Farmer Stratification (cont'd)

Factor or Characteristic Criteria for Stratification Key Contributing Factors



Significantly different system
Interactions which could potentially affect adoption decisions

Differences In the relative weights
given to various goals, particularly
between economic and non-econaiiic

Gender of household head; complex
or nuclear In organization; and
significant difference in the roles
of household members

Low or high market orientation of the

Crop-livestock relationships Competition for resources among activities; input-input Complementarity among activities; input-output Relationships among activities to provide for the various
household goals, eg., cash-cropping or food-cropping;
Disposition of output from various activities

Level of subsistence Household food requirements Stage of houseold development Household's market orientation Household's cash requirements Kinship, religious, ond social obligations Security emphasis may be identifed by cropping diversity
and use of traditional varieties and methods, particularly
on food crops

Stage of household development Roles of men, women, and children in the household
and in the production of agricultural output l4ho controls what resources Means of household decision-making

Present and past use of recommended technologies Participation In development programs Current and past market activity: extent of cash cropping,
use of purchased inputs, food purchases, and acquisition
of consumer goods
Scientific orientation (control over nature)
Attitude toward governent


The preceding discussion illustrates the numerous factors which

are potential sources of variation in adoption of technology among farmers. However, not all of these factors are significant in any one situation. A stratification process needs to be devised through which field teams can identify those factors which are significant in their particular situation. Such a process must be simple enough to be applicable in the field, yet thorough enough to ensure that critical

elements are not overlooked.
An examination of Table 1 reveals the interrelatedness among

factors. It also reveals that certain factors, such as market orientation, reflect a number of other circumstances, many of which are difficult to identify in the field. These revelations are useful in developing a simplified procedure for delineating farmer target groups. The purpose of this chapter, then, is to disentangle and prioritize the myriad factors that influence farmers' adoption decisions into a workable framework for farmer stratification. This framework is illustrated in Section 4.2 for the case of Lesotho's lowlands.

4.1 A Procedure for Socioeconomic Stratification

FSR practitioners generally follow a three-tiered procedure for

identifying farmer circumstances and researchable problems. Delineating

recommendation domains is part of all three steps, but the third step ,is primarily for verification. The first step is to study and analyze secondary data in order to gain a good understanding of the general topographic, climatic, demographic, and institutional environments of the target area. Preliminary target groups are identified from obvious variations in these data.

The exploratory, or informal survey is the second step. Primarily qualitative information is gathered by quick inspection of the research
area and through informal interviews with farmers, merchants, agricultural officers, and village leaders. Most of the information used to delimit recommendation domains is obtained here. Step three, the verification or formal survey, is primarily to gather more quantitative information to verify problems and variations hypothesized from the exploratory survey. It also provides researchers with a more solid base for developing technologies to be tested on fayTners' fields.

Stratification starts with the analysis of secondary data.

Farms can be distinguished according to obvious variations in agroecologic characteristics, access to markets and other institutions, and farm size. Local population densities and labor market characteristics should be identified for purposes of comparison to farmers' individual characteristics later on. Significant variation in population densities and labor market characteristics across the target area should be noted as potential stratification criteria. Different

land tenure arrangements in the target area should be identified as to their potential for differentiating farmers. Different ethnic groups may also be identified here. Information gathered from

secondary data sources help guide researchers in the design and implementation of the exploratory survey. At this point stratif ication is very tentative; farms as physical units and farm ing areas may be stratified, farm households, though, are not.

Stratification of farm households really begins with information gathered in the exploratory survey, though information from secondary

data sources is still very useful. It is easiest if farmers are first grouped according to parameters which have the fewest alternatives and are easiest to identify. Also, other factors nay only be important for certain groups, such as small farmers with low market orientation. With this in mind, the following stratification procedure is proposed. (See Figure 2)

Farm size is a good stratification criterion with which to

begin. It is relatively easy to identify and is easily understood by

research and extension personnel . Differing farm sizes reflect different resource endowments, different goals and motivations, and

perhaps different risk orientation. At this point, the distinction should only be made between large, commercially oriented farmers and

medium and small farmers. Large farmers generally have high market orientation, indicating good institutional access, and likely have had past experience with introduced technologies, suggesting a willingness to experiment and change. Separating out "large" farmers at this point is an attempt to capture all of these differences which usually occur together. Differences within this large farmer group, resulting in different technological requirements, are primarily agro-economic rather than socio-cultural. The actual distinction between medium and small farmers and large farmers is relative and

Topography and climate Popula ion densities, Institutional enviroment . Preliminary Reconmendation Domains
Land tenure ( Derived from Secondary Data Sources
Local labor markets Seasonal migration patterns)

Total farm area Farm Size Large with high
Cultivated area. -7 I mrket activity
& small

Market access
Market activity Past technological --- -. -CMarket Orientation -Hig
experience (
Income Low
Membership in farmer
organization I
Ethnic Group

a ule- or Male-Headed

Owership of tractor
flu. draft antmm1 / .I'

Hired traction Condition of machinery Health of animals

Cropping patterns and intensity

4 Power Source Differences

System of rotation- - -------- Current Farming System Differences
Fragmentation ( 1 and Differences in Potential for Change
Animal husbandry
System interactions
Changes over time

Possible Sources of

Landholding Labor Constraint Different
Off-fare income Land Constraint Technological
Household size Cash Constraint lRequireents
Household composition Verify Food Needs &
State of development --Sources of-- Preferences
Role distinctions Variation Cash Requirrients
Differential control Security
of resources Social Obligations
Decision-making Tenure Arrangemnts

Interactions among groups -- RCOtIMENDATION D9(INS

Verified by foral survey
and refined if necessary

Note: Box indicates where a stratification decision is made.

Figure 2. Procedure for Socioeconomic Stratification.


must be made in light of the particular situation. Further division of small- and medium-sized farmers on the basis of size is not necessary at this point because absolute farm size is not as important as farm size related to other farm resources. Further division along other lines is probably more useful.

Farmers with small- and medium-sized operations can then be
stratified according to their degree of market orientation. Market orientation reflects two important factors, market access and resource endowments, as well as a farmer's level of subsistence, motivations, and risk orientation. A high degree of market orientation indicates
good institutional access, at least adequate resources to overcome major constraints, high economic motivation vis-a-vis subsistence or security goals, and limited aversion to risk. Low market orientation probably indicates the opposite is true in at least one of these areas.

Farmers with high market orientation are apparently not as influenced by social and cultural norms as are more subsistence

oriented farmers (Noman, et al., 1982; and others). Their circumstances are probably more akin to those of larger farmers, and they may be grouped with them at this point. If research is to be targeted at this group, further distinction can be made based on agro-economic

variations wfiich indicate different technological requirements.

When deciding whether a farmer has a high or low degree of

market orientation, researchers should be conservative and, when there is doubt, include the farmer in the low group. An error in this decision would be less costly to the fancier if he is wrongly

included in the "low" group rather than if he is wrongly included in the "high" group.

Small and medium-sized farmers with low market orientation are usually the focus of development research in low income countries. Many socioeconomic and cultural factors influence adoption decisions in this group, so stratification becomes more important and more difficult. Thus, an early distinction should be made according to ethnic group. Many important socio-cultural differences such as food preferences and social obligations may be explained by ethnic origin.

Female- and male-headed households should also be separated into different recommendation domains. Many contemporary Women in Development experts contend that female-headed households react differently than do those headed by males (Kervin, in KSO, 1981; Fresco, 1979). For one, women-headed households generally are more resource-poor. However, even given similar resource endowments, it is argued that women tend to react differently than men because of their socialization. For example, they probably act more cautiously than do men and they generally place a greater emphasis on household needs and are less

likely to make investments in the farm. Further, in most cultures, women probably have lesser institutional access and may not have the respect accorded to men.

Now is the time to begin careful examination of the different farming systems. A first source of variation to be looked for is difference in power source. Power is critical to most farming operations, it is commonly used in current stratification strategies, it is relatively easy to identify, and it has a limited number of alternatives. Source of power available to the farmer is the major concern

here, although a farmer's choice of power reflects other constraints in the cropping system and household as well.

Other differences in farmers' current farming system. s need to be identified. These include differences in enterprises, differences in management practices such as cropping patterns, intensity, rotation, fragmentation of fields, and animal husbandry, and differences in interactions within the system. Hypotheses on the sources of these variations are developed. Variation may be due to constraints on labor, land, or cash; they may be due to differing food needs and ] references, cash requirements, security needs, or social obligations; or they may be the result of different tenure arrangements. These hypotheses then need to be verified by examining household characteristics, including household size and composition, stage of household development, role distinctions and differential control of resources, differences in decision-making, and farmer's perceptions and attitudes.

A final consideration is interaction between the different
groups of farmers identified by the field team. Farmers involved in reciprocal and cooperative arrangements may need to be grouped differently, depending on the nature of the agreements and their

influence on the farmer's decisions.
At this point the field team should be fairly confident of their delineation of recommendation domains and the research staff can

begin to fonnulate recommendations for each domain. Necessarily, decisions must be made on whether variation is significant or not. This is where the "artistic" skill of the researcher is important. Following the formal survey, recommendation domain boundaries should

be refined if necessary. However, with a good exploratory survey, little adjustment is usually needed (Collinson, 1982).

This procedure for stratifying farmers into recommendation

domains is summarized in Figure 2. The order of the decision criteria
may vary in some situations to reflect differences in the relative importance of certain factors. It should be obvious to the reader that, should farmers be stratified according to each of the potential differentiating factors, an unworkably large number of domains will result. This should not happen for three reasons. First, significant sources of variation will probably be limited to just a few factors in any given situation. Second, policy decisions regarding groups to be targeted and groups which are too small to be significant will limit the number of recommendation domains which are ultimately considered. And third, the type and scope of potential recommendations may allow No or more recommendation domains to be combined into one. The following case should illustrate these points.

4.2 The Case of Lesotho's Lowlands

Lesotho's lowlands are generally north-south in orientation and are low only relative to the mountains. They lie 5,000 to 6,000 feet in altitude and are characterized by deep gullies (dongas), severely eroded hillsides, sandstone cliffs, little flat land, and, thus, a very limited arable area. The climate has four seasons with frost and occasional light snow in the winter and irregular rainfall in the summer, coming mostly in violent thunderstorms with frequent hail.

Most of the soils are marginally suited for cultivation and the grasslands are severely overgrazed.

Agriculture is primarily small, subsistence farming. The main staple is maize with sorghum, also produced for domestic consumption. Until recently, traditional land tenure prevailed where farmers maintained only usufruct rights to the land and allocation of these rights was through local chiefs. Although a new Land Act was recently adopted which provides for long-term leaseholds that allow farmers to borrow against their holdings, farming still reflects the traditional tenure system. Most households farm two or three scattered fields of varying quality. As a result of traditional land allocation there are very few "large" holdings. In fact, due to repeated subdivision of holdings, most rural Basotho cultivate quantities of land inadequate to meet subsistence needs.
Insufficient returns from farming are supplemented by remittances from migrant labor in the Republic of South Africa. Half the male work force and about 20% of the female work force are absent from Lesotho at any one time. A full 60% of the households receive income from external labor migration and another 10% receive income from wage employment in Lesotho (van de Wiel, 1977). While the future prospects for migrant labor are uncertain as South Africa undergoes internal changes, off-farm income will continue to be significant for rural Basotho households.

The follow ng example of a stratification of farmers in lowland Lesotho is presented for illustration only. While an accurate representation of farming is this area is attempted, certain assumptions

are made in the absence of data that a research team would normally acquire. These assumptions are made based in part on my own experience in Lesotho. The data used in this case are from Toward the Year 2000: Strategies for Lesotho's Agriculture by Eckert (1982) unless otherwise specified. These data were compiled from a survey of the Thaba Bosiu Project area but Eckert concluded that they were fairly representative of Lesotho's lowlands.
Analysis of available secondary data is the first step in determining recommendation domains. For the lowlands of Lesotho, this analysis yields the following information. During the summer growing season, the northern part receives significantly more rainfall than the central and southern areas. In the south, rainfall is more variable and humidity is lower. The length of the growing season (defined by the average number of frost-free days) declines significantly from north to south. In the north, there is a predominance of richer, deeper soils with higher moisture holding capacities. In general, then, growing conditions become poorer with movement from north to south. Yield data confirm that there is a comparative

advantage for maize in the north and for sorghum in the south. These several differences acting together probably indicate that the lowlands of Lesotho should be preliminarily divided into north, central, and south regions. Their boundaries should follow current district boundaries for easier administration.

Certain other potential sources of variation are also revealed from the analysis of secondary data. This information helps to direct the research team in its search for significant variation among farmers. The high incidence of migrant labor means that

off-fam income is probably significant for stratification. Van der Wiel (1977) estimated that 34% of the households in Lesotho are headed by women and that 68% are managed by women in*their husband's absence. Gender of the household head, then, is likely significant as well. Since Lesotho is basically populated by one ethnic group, this distinction is not necessary; and since there are virtually no

large farms, farm size is not important either. (This would not be the case where accumulation of landholdings is possible.) .

The importance of cattle to farming in Lesotho also becomes

apparent from secondary analysis. Ownership of cattle allow a household to be self-sufficient in draft power and have control over the timing of plowing and planting. This is particularly important in light of Lesotho's single growing season and uncertainty of the first spring rains. Finally, it should be noted that 16% of rural lowland households do not have access to land, and that a significant number are not accessible by road, limiting potential market activity.

The extent that potential variation is significant, and the
number of households in different possible groups can be ascertained from data gathered in infonnal surveys. (However, in this case, data were gathered in formal surveys.) This information is used to define possible recommendation domains while policy will dictate the ultimate delineation. Although this illustration is for the central lowlands, similar procedures could be employed in the northern and southern regions. The decision tree in Figure 3 portrays this procedure for stratification of farmers in the central lowlands of Lesotho.

Although most agricultural production in Lesotho is for
subsistence, there are a number of farm households which are active

1.1 Landless 16ah

1.2 High market

2.1 labor surplus 5%
(<0.4 ha/cap.) (6%)
18% 3.1
(21%) 2.2 land surplus 13% < 3,
(>0.4 ha/cap.) (15%) 3.2

cattle only 4%
cattle and tractor 9%


2.3 Male-headed 41% (48%)<3.

1.3 Low market 66%
orientation (79%)

2.4 Female-headed 26%

No cattle 27% K

cattle 14%
(17%) cattle 7%

No cattle 19%

4.1 No off-fam 6%
income (9%)

4,2 off-fanm 21%c 5.2
income (25%)
4.3 off-farm 14%c 5.3
income (17%) 5.4

4.4 off-farm 13% -z-5.5 / income 16%)


labor-surplus 12% (14%)

land-surplus 9% (11%) labor-surplus 1q% (12%) land-surplus 4% (4%)

labor-surplus 9% (Ir%) land-surplus 5% (5%)

no off-farm 13% income (15%)

bPercentages are of all rural households. Percentages in parentheses are of landed rural households.
An estimated half of 4:2 and 4.3 are female managed, or a total of 18% of all rural households.
Note: Percentages may not add up due to rounding.

Figure 3. Stratification of Rural Households in the Central Lowlands, Lesotho.

in agricultural markets. Ideally, these households would be identified by primary data on the extent of their market involvement. In

the absence of such information, fam resources are used to identify those households which are likely to be highly market oriented.

Households which have off-fam income and own cattle with yoke and plow, cultivator, harrow, and planter, and households with off-farm incane and which have cattle with yoke and plow and adequate land (greater than 0.4 ha. per household member) to produce a surplus are

considered to have high market orientation. Some of these households have access to tractors as well.

The highly market-oriented farm households comprise 21% of rural landed households or 18% (21% of 84%) of all rural households.

(Henceforth, percentages given are of all rural households.) They are primarily headed by males so gender is not considered for this group.

Eckert (1982) considered that the amount of land per household member was significant to farming in Lesotho. He estimated that at least 0.4 ha. per capita was necessary to be self-sufficient in cereals and have the potential to produce a surplus. This figure

could be lowered slightly if the famer had other resources adequate to farm intensively. Households with less than 0.4 ha. per capita are described as labor-surplus while those with greater than 0.4 ha. per capita are land-surplus.

Less than one-third of the households with high market orientation were labor-surplus. This group was 5% of all rural households. Land-surplus, highly market-oriented households were 13% of all rural households.

Further distinction of market-oriented farmers might be made according to whether or not they have access to tractors. From a baseline study, tractors were found to have been used on 15% of the fields in one project area (van der Wiel, 1977). Assuming (1) this is representative of the lowlands in general, (2) that only a small portion of these fields would not be included in my grouping of market-oriented farmers (to include them would raise the percent of landed households with high market orientation to, say, 25%), and (3) that the skewness of land distribution means that 15% of the fields represents less than 15% of the households, I estimate that about

half of the market-oriented farmers have access to tractor power.

In the case of labor-surplus households, tractor power is probably not significant since their farming is labor intensive. The proportion of households using tractors in labor intensive operations is likely to be small. Therefore, maybe two-thirds of the land-surplus, market-oriented households used tractors, or about 9% of all rural households.
At this point, the stratification process has yielded three

recommendation domains for market-oriented households: (See Figure


1. labor-surplus, market-oriented households: 5% (2.1 7 in

Figure 3)

2. land-surplus, market-oriented households with no tractor

access: 4% (3.1 in Figure 2)

3. land-surplus, market-oriented households with tractor

access: 9% (3.2 in Figure 3).

7Refers to the cell number in Figure 3.

Franzel (1981) stated that a group of farmers comprising at least 10% of the farmers in an area is significant enough to be a

separate recommendation domain. This see-ns to be rather arbitrary, but research budgets certainly limit the size and number of domains to be targeted. In this case, policy considerations may dictate that the first two are too small to be considered. In the case of marketoriented farmers, per capita landholding is more important in determining cash cropping potential than is type of traction, particularly since

all these farmers do have animal power. In Lesotho, farners produce cereals (mainly wheat), legumes, and, in some cases, vegetables for market. Commercial production of cereals requires adequate land resources; one would expect labor-surplus households to have a comparative advantage in commercial production of legurnes and vegetables which require a significantly larger labor input per hectare.

For this reason, it would be appropriate to combine the two

land-surplus domains, leaving two domains of market-oriented farmers:

1. labor-surplus, market-oriented farmers: 5% (2.1 in Figure 3) 2. land-surplus, market-oriented farmers: 13% (2.2 in Figure 3). Farmers in each of these domains have different cash cropping potential. Given the non-land resources of farmers in the first domain, they likely farm intensively and may cultivate other fields through a sharecropping agreement. Farmers in the second domain are more likely to farm extensively. For this group, the fact that a significant proportion do not have access to a tractor should be remembered since this affects their ability to plant their crop on time.

It should be noted that the number of market-oriented households
might actually be greater than indicated here. Substantial off-farm

earnings, sharecropping, and cattle exchange make it possible for some households, who otherwise couldn't, to purchase inputs and food

and sellI produce.

For households with low market orientation (66% of all rural

households, or 79% of landed households), more factors normally need to be considered for stratification. Lesotho is no exception. Gender of the head-of-household is an important distinguishing feature. (See Figure 3.) 1 estimate from Eckert's (1982) data that maleheaded households with low market orientation comprise 41% of all rural households; female-headed of the same group make up 26%.

Ownership of cattle is important for a household's control over

land preparation and timely planting. Male-headed households should be divided into those with cattle (implying self-sufficiency in draft power) and those without. The number of female-headed households with cattle is small (7% of all rural households). And since women do not normally handle livestock, whiich means that plowing must still be contracted, differentiation based on cattle ownership is not significant for households headed by women. (This distinction is still shown in Figure 3 in order to present more information.)

Since many non-market-oriented farmers in Lesotho can be considered as part-time farmers, off-farm income is particularly important. The data indicate that there are very few (if any) households in Lesotho that have cattle and no off-farm income. This is reasonable since the main reason Basotho migrate to work off the farm is to accumulate assets for the future, and the first assets they tend

to acquire with their earnings are cattle. Therefore, only maleheaded households with no cattle and female-headed households are

divided according to wfiether they have a source of off-farm income. (See Figure 3.)

Since non-market-oriented farmers generally do not farm intensively, relative per capita land areas are not as important for this group. However, this information may become important as technological improvements are adopted and some farmers become more active in the market. For this reason, those households which can potentially become market oriented (those with cattle and off-farm income, 4.3

and part of 4.4 in Figure 3) are further divided into labor-surplus and land-surplus households. Even households without cattle may have surplus land which can potentially produce food for Lesotho's fooddeficit economy. These households should be identified to aid development planning (4.1, 4.2, 4.5, and part of 4.4 in Figure 3).

Also important for this group of potential market actors is

whether the farm is accessible by road. Road access is necessary for the farmer to market significant surplus production. Much of Lesotho's lowlands are not accessible by road but the number of households farming in these areas is unknown. Hence, road access is not used in this stratification. However, its importance must be considered when planning research for this group of farmers.

Although per capita landholding is not used to define recommendation domains initially, the proportions which are labor surplus and

land surplus are noted for relevant domains in the event that further delineation along these lines is found to be desirable. The stratification of households with low market orientation yields the following recommendation domains: (See Figure 3.)

1. male-headed households with no cattle and no off-farm

income: 6% (4.1 in Figure 3.1; primarily land-surplus)

2. male-headed households with no cattle but with off-farm

income: 21% (4.2 in Figure 3; labor-surplus, 12%; landsurplus, 9%)

3. male-headed households with cattle and off-farm incomes:

14% (4.3 in Figure 3; labor-surplus, 10%; land-surplus, 4%)

4. female-headed households with off-farm incomes: 13% (4.4

in Figure 3; about half have cattle; labor-surplus, 9%;

land-surplus, 5%)

5. female-headed households with no cattle and no off-farm

incomes: 13% (4.5 in Figure 3; primarily land-surplus).

According to van der Wiel (1977), a large number of households headed by absentee males are actually managed by women. Thus, it is estimated that half of the male-headed households with low market orientation and with off-farm incomes (4.2 and 4.3 in Figure 3) are actually managed by women. (See page 76.) In Lesotho, this generally means that women make the day-to-day decisions but not the larger, one-time investment decisions. This group of male-headed, femalemanaged households comprise about 18% of all rural households. This group is significant since decisions to adopt recommended technologies are most likely made by a household member who is frequently absent and who is not actively invol ved in the daily operation of the farm.

Two of the recommendation domains of non-market-oriented farmers are probably not in a position to benefit directly from agricultural research. These are the male-headed and female-headed groups with no

cattle or off-farm incomes (4.1 and 4.5 in Figure 3). They are probably welfare households and can be combined into one domain. Potential programs for this group might involve increased off-farm labor opportunities. Under the new land act, they may also be able

to lease their underutilized landholdings to those farmers with superior non-land resources.

Fran this stratification of rural households in the central

lowlands of Lesotho, eight recommendations domains have been identified. Two of the groups should be of vital concern to development planners but would not be targeted for agricultural development:

1. Landless households: 16% (1.1 in Figure 3)

2. Households with n6 cattle and no off-farm income that are

primarily labor deficit: 19% (4.1 and 4.5 in Figure 3).

The significance of these two groups is that 35% of all rural households are not likely to benefit from efforts to increase agricultural production.

Two other recommendation domains have been identified as market oriented:

3. Labor-surplus, primarily male-headed households: 5% (2.1

in Figure 3)

4. Land-surplus, primarily male-headed households: 13% (2.2
in Figure 3; households with access to tractor power are

9%, 3.2 in Figure 3).
These households all have cattle and off-farm incomes and they have sufficient amounts of other resources to likely be active in the marketplace. These are generally the more well-off households in

Lesotho. Efforts to increase aggregate food production will probably be targeted at these groups.

Households with low market orientation are divided into four recommendation domains:

5. Male-headed and managed households with no cattle but with

off-farm incomes: 10% (half of 4.2 in Figure 3; over half

of these have surplus labor)

6. Male-headed and managed households with cattle and off-farm

incomes: 7% (half of 4.3 in Figure 3; over 70% of these

have surplus labor)

7. Male-headed but female-managed households with off-farm

incomes: 18% (5.1 in Figure 3)

8. Female-headed households with off-farm incomes: 13% (4.4

in Figure 3; almost two-thirds of these have surplus labor). Together, these groups make up 47% of all rural households. They are primarily part-time farmers, relying on supplemental off-farm remittances. Efforts to improve the welfare of rural households through agricultural development should be directed at these groups. Those with cattle and landholdings surplus to subsistence needs might be expected to produce a surplus with improved cultural practices.

It must be stressed that stratification of farmers into recammendation domains is an iterative process. The decision tree in Figure 3 provides a substantial amount of information whiich allows domain boundaries to be adjusted as new information is obtained, as production constraints are identified, and as development goals and directions are refined.


5.1 Summary

This thesis sought to: 1) review and codify current FSR stratification approaches, 2) identify socioeconomic factors which are important when stratifying small farmers, and 3) develop guidelines for systematically including socioeconomic factors in small farmer stratification.

Rationale for stratifying farmers is threefold. One, effective research can only be conducted for a similar group of farmers. Two,

extrapolating research results to other similar farmers improves the cost-effectiveness of farming systems research. And, three, recommendation domains can aid in formulating policy and development alternatives. A recommendation domain is a relatively homogeneous

group of farmers for whom more or less the same recommendations can be made. Farmers in the same domain should have the same researchable problems and development alternatives, and should react in the same way to policy and technological changes.

Two assertions are made regarding the identification of factors to be considered in farmer stratification: one is that critical

factors influencing farmers' decision concerning adoption of agricultural technology are the factors which should be considered for
stratification. The other is that socioeconomic and cultural factors

are as important, and in some cases more important than technical factors in explaining differences in farmers' adoption decisions. Both assertions are intuitively acceptable and evidence presented in this study supports them.

In light of these assertions, the following hypothesis was

offered: that incorporating socioeconomic and cultural factors into the farmer stratification process in Farming Systems Research increases the likelihood that research recommendations will be successfully adopted by client farmers. Evidence and theory presented in this paper support this hypothesis. An empirical illustration using Lesotho data demonstrated the feasibility of this precept.

An extensive sample of FSR literature was reviewed to determine

which stratification strategies were commonly employed. Two observations emerged. First, while socioeconomic factors were often considered when identifying representative farmers and planning research, stratification was frequently done along agro-climatic lines. Second, as stratification becomes more of an integral component in the research process, socioeconomic factors have been more frequently considered.

Socioeconomic factors appearing most frequently in the literature were identified. Variations in farmers' current cropping systems were frequently employed as stratification criteria. These variations were primarily differences in farmers' management practices and production. The farmer's resource base vas frequently used, with farm size and power source most often mentioned. The institutional

climate, markets in particular, was considered in most strategies. Household characteristics, including farmers' goals, were rarely considered.

Various socioeconomic factors were analyzed which, hypothetically influence farmers' decisions concerning agricultural technology.

Twelve factors Aich appear to be most influential for farmer stratification were isolated. These were 1) market access, 2) information
source, 3) ethnic or class differences, 4) interactions among famer groups, 5) labor, 6) power source, 7) liquidity, 8) land, 9) farming system interactions, 10) household goals, 11) household composition, and 12) household orientation. In addition to influence on adoption behavior, extent of influence on other factors, the likelihood that the factor might be overlooked, and ease in identifying variation were considered as well when selecting these twelve factors. Other factors (such as access to institutional credit and extension) were not included because the evidence on their relationship to farmers' decisions was mixed with general conclusions being that they did not account for significant variation in farmers' pract ices. Or they were not included because they are implicitly accounted for in one or more of the included factors. Risk factor was not included for this reason.

Finally, a simplified procedure for socioeconomic stratification of farmers was developed on the basis of conclusions derived from the

analysis of these twelve socioeconomic factors and from the review of current stratification strategies. This procedure was shown to be
feasible by illustrating the stratification of lowland farmers in Lesotho.

5.2 Conclusions

Several conclusions can be drawn fran this study. First and foremost is that socioeconomic and cultural considerations are very

important to farmers' technological decisions and must be considered in any farmer stratification scheme. Currently, they are not sufficiently considered. The approaches that are the best in this regard are those patterned after the CIMMYT strategy, particularly those employed in Eastern Africa. It seens apparent that as stratification increases in importance and becomes more formalized in approach, the

importance of the socioeconomic element will also increase as a basis for stratification.

The relative importance of certain household characteristics has been demonstrated here. They need not necessarily be considered as stratification criteria, but they must at least be examined as possible explanations of farming system variations. Variation itself is not what is important; rather, the causes of that variation are what affect farmers' reactions to potential changes. If, for example, labor is constraining and a new method is proposed to alleviate that constraint, the reasons for that labor constraint determine whether or not the new method is acceptable. While all possible sources of variation cannot be included in the stratification criteria, the

research team, together with local farmers and agricultural officers, must decide which sources are important in any given situation. In other words, many factors must be examined before the few, most important ones are isolated. The complexity of the stratification

issue must be understood before a simpler procedure can be successfully employed.

Another conclusion drawn here, and in other recent farming

systems work, is the importance of system interactions. These include interactions within a single farming systEm as well as interactions

between different farming systems and between a farming system and the larger community and regional systems. The nature of such interactions can explain why some farmers adopt a recommendation wilile others do not. The interactions are more complex in traditional farming systems, thus, an understanding of them is more important.

5.2.1 Policy Implications. Recommendation domains are discussed in this thesis primarily as a tool for research. It is argued that correctly identifying homogeneous farmer groups improves research results (adoption of recommendations) and makes research more costeffective because of improved adoption rates and increased ability to extrapolate research results. A recommendation domain approach may have significant policy implications as well. Entire countries or regions could ultimately be stratified into recommendation domains. Scarce resources could be allocated according to stated policy goals to reach either the poorest people, or the most people, or to achieve self-sufficiency in a certain crop or increase the production of an export commodity. Clear identification of the different groups of producers make design of research strategies and implementation of policy decisions easier.

In the same vein, clear identification of target groups, and inclusion of all rural households into one or another groups, can help ensure that no household is overlooked. This may force financially strapped governments to make some hard choices, but at least no families are left out by default. Even if a distinct group comprises less than 10% of the households in'an area and does not therefore qualify as its own domain, as Franzel (1981) suggests, surely there are other similar households in other areas. Together they may

Full Text
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