Technology Transfer and Structural Adjustment in U.S. Agriculture
Nicholas G. Kalaitzandonakes and Bruce Bullock
Department of Agricultural Economics
University of Missouri-Columbia
In recent years, important changes have occurred in the process by which technology and
information are transferred to U.S. agriculture. Private management consultants and industry sales
representatives have become increasingly important in providing information to farmers (Sonka).
Much of the technical knowledge traditionally transferred to agricultural firms by Land Grant
Universities and the Cooperative Extension Service has become part of "technology packages"
offered by input suppliers, agricultural cooperatives, independent consultants, and agricultural
entrepreneurs. Contract production and precision farming are examples of such "technology
The changing relative importance of private and public sectors m agricultural technology transfer
have been largely motivated by technical and organizational innovation as well as significant
structural change in the agricultural sector. Several studies have discussed the effects of structural
changes in the agricultural sector on the public research and technology transfer system (Busch
and Lacy, Buttel et al., White). Yet a satisfactory conceptual framework that ties the structure of
the agricultural scttor to that of the public technology transfer system is still unavailable.
The dominant paradigm of agricultural transfer remains the rational model which assumes that
where scope for economically beneficial technology transfer exists, such transfer will in fact
occur. This assumption implies that: (1) the research and technology transfer system is always
aware of the end-users: technical needs and, science permitting, will always generate technologies
that address them; and (2) the end-users always understand the benefits of technologies generated
by the technology transfer system and, given enough time and capital, will eventually adopt them.
This paradigm of technology transfer has followed the Solo'ian tradition of exogenous technical
change. As a result, the research and technology delivery system is exogenized in te rational
model of technology transfer. Insistence on this theoretical model is exemplified by the numerous
technology adoption and diffusion studies carried out by agricultural economists and rural
sociologists over the last forty years. In these studies, the technology transfer process is reduced
to a one way causality where "good" managers (e.g.educated, experienced, etc.) are expected to
adopt new technology faster. The appropriateness of the technology generated and transferred is
rarely questioned and the modes of delivery are obscured. The linkages between the research and
technology transfer system and the end-user are ignored. Thus, there is little theoretical basis for
judging how structural adjustments in agriculture may affect the research and technology transfer
This paper develops a conceptual framework of technology generation and transfer based on the
theory of transaction costs. The dynamics of technology generation, delivery, and adoption are
reviewed within a unified non-linear agricultural innovation model. Within the proposed
Draft Document For Restricted Circulation
framework one can then begin to tie the structure of the agricultural sector to that of the research
ard te-chnology transfer system, and, ultimately the direction of innovation.
The Rational Model of Technology Adoption and Diffusion
Introduction of any new agricultural technology is typically met with only partial success. Many
members of a population of potential adopters adopt slower than others while some never adopt.
Such behavior gives rise to innovation diffusion which typically follows rather standard sigmoid
temporal patterns (Grilliches). Due to such regularities, innovation adoption and diffusion studies
in agriculture have focused on comparisons between adopters and non-adopters in an attempt to
identify systematic differences in economic, social, demographic, institutional, and locational
characteristics of each group (Rogers, Rogers and Shoemaker, Feder et al.).
The conventional wisdom of most of these studies i- that constraints to rapid innovation adoption
involve factors such as lack of credit, limited access .0 information, aversion to risk, inadequate
firm size, insufficient human capital, absence of equipment to relieve labor shortages, irregular
supply of complementary inputs, and inappropriate infrastructure (Feder et al.). Implicit to such
adoption and diffusion models is the assumption of exogenous innovation and technology
generation process. No direct influence is exerted by the recipient of the technology on the
innovation process. The research establishment is assumed to add to the stock of scientific and
technical knowledge which, presumably, is always appropriate to the needs of the potential end-
users. Hence, the rational econor.ic agent should unquestionably adopt the innovation in the
absence of exogenous constraints or inadequate managerial skill. With such pro-innovation bias,
the emphasis is clea-'v on the end-user (Abramson, Goss). In this model the innovation process
is treated as separable from the adoption and diffusion process and need not be explicitly
The assumption of exogenous agricultural technolc-y has been rejected in many studies. Gutman,
Huffman and Miranowski, and Evenson and Rose-Ackerman provided empirical evidence where
farm interest groups were found to exert influence on the funding and direction of agricultural
research and technology transfer. Similarly, Busch and Lacey concluded that granting agencies,
agribusiness firms, foundations and other organizations exercise significant influence on the
direction of public agricultural research primarily through grants, contracts and consulting funds.
In addition to being misleading, the assumption of exogenous innovation is also limiting. In
effect, it conceals the linkages of the agricultural research and technology transfer system with its
end-users. As a result, the bulk of the agricultural technology transfer literature provides little
guidance about the impact of structural change in agriculture on the technology transfer system.
-Even studies that explicitly recognize the endogeneity of innovation, lack a unifying theory that
ties the structure of the research and technology transfer system to that of the agricultural sector.
A Non-Linear Model of Agricultural Innovation and Technology Transfer
A step in the right direction for analyzing inter-dependencies between the technology transfer
sy ,;em and the agricultural sector would be a non-linear agricultural innovation model. Non-linear
innovation models have been developed to capture observed simultaneous "demand-pull" and
"science-push" influences on innovation (Kline, Mayers and Marquis). These models are
motivated by and structured around empirical findings. Non-linear innovation models should be
contrasted with linear models which reflect a sequential process whereby science push leads
always to more innovative activity without regard to any selection mechanism.
A hybrid non-linear agricultural innovation model can be constructed by borrowing from non-
linear models of industrial innovation and using empirical findings and stylized facts from a host
of technology transfer studies in agriculture. A significant shortcoming of existing non-linear
innovation models is that they fail encapsulate the tacit dimension of technology (Senker). As
empirical evidence is mounting that the tacit dimension of technology is a primary determinant
of success in technology transfer, it becomes necessary for non-linear models to explicitly allow
for such dimension (Pavitt),
Accordingly, the basic premise of the innovation and technology transfer model presented here
is that technology transfer is inhibited primarily by informational and knowledge barriers. These
informational deficiencies reflect inherent difficulties in replicating, adapting and transferring
tacit technical knowledge across individuals, organizations, cultures, institutions, and geographical
locations. The proposed model is illustrated in Figure 1. Technology transfer is assumed to
involve three separate phases: (1) opportunity recognition, (2) technology design and generation,
and (3) adoption and diffusion.
Opportunity Recognition: In this first phase, a need or opportunity for implementation of new
technology is identified. A variety of exogenous factors generate opportunities for creation and
use of new technology including (Drucker):
random occurrences (e.g. a disease outbreak);
changes in demographics or consumer tastes and attitudes;
changes in the institutional and regulatory environment;
changes in scientific knowledge.
A need/opportunity for implementation of new technology may be recognized and articulated by
the end-user (a "demand-pull" situation), Alterntively, a perceived need/opportunity for
technology may be articulated by the source of the technology or the research establishment at
large (a "science-push" situation). In this latter case, technology may first be generated and
suitable end-users may subsequently be searched for.
Opportunity recognition is enhanced by factors that: (1) increase awareness about the state of the
'technology-in-use vis a vis the state of the art, (2) facilitate the flow of information between the
source of the technology and the end-user, and (3) create pressures for change and innovation.
Some of these factors are intrinsic to the end-user and to the source of the technology. For
example, entrepreneurial and well informed managers of private firms recognize opportunities for
new technologies early on and actively pursue them. Similarly, technology transfer organizations
Adoption and Diffusion
Random events Relevance
Demographics Rv,,e __New
Deinsiutons _iOpportunity -* of-
New Knst wle dgerU OpponTechnology
Understand of User Needs
with extensive scientific expertise and in-depth knowledge of the production/distribution systems
of potential end-users are likely to readily identify opportunities for technical change as they arise.
Organizational and institutional factors that improve communication between the technology
source and the end-user can also facilitate opportunity recognition. For example, cooperatives and
producer associations tend to create forums where technical needs/opportunities are more readily
identified, communicated and empowered (Stratz). Similarly, technology transfer organizations
with focused clientele (e.g. commodity research groups) enjoy relatively swift information flows
which assist opportunity identification. Competitive market forces, environmental regulation, or
compelling societal needs are examples of exogenous pressures that may stimulate active pursuit
of new technologies and improve opportunity identification (Kalaitzandonakes and Taylor,
Kalaitzandonakes et al).
Technology Design and Generation: Clearly, not all identified opportunities are equally important.
The significance of any opportunity identified is in effect determined by the implied degree of
technical advancement and socioeconomic impact. The potential impact on such factors as food
security, employment, and growth typically determine the economic importance of any identified
opportunity for technical advancement. Non-economic factors, such as the political clout of the
end-user, may also impact the importance attached to an identified opportunity (Huffman and
Miranowski, and Evenson and Rose-Ackerman).
The aggregate effect of all the factors that influence the process of opportunity identification is
expressed by the strength of the signal that arrives at the source of the technology. Many different
identified opportunities are communicated to or articulated by the source competing for scarce
research and transfer resources. The stronger signals indicate greater opportunity. However, the
final decision to turn an identified opportunity into a research project lies within the technology
transfer organization. As a result, the organization's culture aind value system will influence this
The value systems of research and technology transfer organizations are typically quite complex.
Researchers and administrators tend to be motivated by, among other things: personal and
institutional recognition (usually manifested by publications, individual vitae, institutional
prestige, etc); personal and institutional profiting (through salaries, consulting, grants, and
contracts); institutional mandate; scientific curiosity; stewardship to natural resources; and
altruism. Hence, any of these factors, especially those nurtured by the culture of the organization,
may have a substantial impact on which identified opportunity becomes a research project.
After the decision to proceed with a particular opportunity has been made, designing and
-generating a technical solution completes the second phase. The constraints and capabilities of the
source are important in designing and generating a fitting technical solution to the opportunity
originally identified, Such constraints include:
* individual and organizational expertise;
o R&D infrastructure;
* organizational forms.
As technology has become increasingly science-based, maintaining sufficient individual and
institutional expertise and R&D infrastructure (e.g. labs, computing power) is important to
research and development capacity. The ways physical and human research resources are
organized (e.g. by product, process, and discipline) are also important. While no single
organization form is dominant under all circumstances, it is well established that organizational
forms can have substantial impact on the R&D and delivery capacity of technology transfer
organizations. The second phase is complete when the new technology has been produced and is
ready for delivery to the end-user.
Adoption and Diffusion: The third phase of technology transfer involves the delivery of the
technology and its adoption by end-users. The relevance of the technology to the end-user's needs
is an important determining factor of adoption, New technologies that address real problems are
usually well received by end-users. Conformity of the new technology with the end-user's
constraints and capabilities is equally important.
Constraints and capabilities which define the capacity of the recipient to use the new technology
include (Feder et al.):
S management abilities;
S own resource base (e.g. degree of capitalization, labor availability);
S supporting institutions (e.g. markets, credit availability);
S culture and traditions;
Compatibility of the new technology with existing resource base, public and private infrastructure,
culture and supporting institutions reduces the possibility of bottlenecks in implementation and
hence it increases the potential of adoption. Collateral assets tend to have a similar impact.
Collateral assets are physical and non-physical assets which in reference to a specific technology
act as complementary or supporting assets (Utterback). For example, new technologies that make
use of tools and implements already owned by the end-user and a set of skills that requires little
adjustment are likely to be well received as they imply little change in the user's production
paradigm (i.e. habits, knowledge base etc). On the other hand, technical innovations that require
major change in the skill set and/or render owned resources obsolete are likely to be met with
However, even when the potential benefits from the new technology are well-understood and
appreciated by the end-user, the important task of effectively delivering the new technology still
remains. A variety of modes and mechanisms can be used to deliver new technology at varying
costs and degree of effectiveness. These include:
Passive dissemination of information (e.g. technical reports, news releases, journal
articles, fact sheets);
Active dissemination of information (e.g. workshops and seminars,continuing education,
* Licensing of technology;
* Personnel transfers and exchanges (e.g. staff transfers, staff consulting);
* Collaborative research and development (e.g. cooperative R&D, R&D contracts);
* Advisory groups (e.g. technical review groups, advisory boards)
The varying degree of effectiveness of these mechanisms is due to the tacit nature of technical
knowledge. Certain delivery mechanisms can facilitate the way in which people communicate,
work together, and learn from each other. Active one-on-one communication is generally superior
to passive delivery of information (Pavitt, Senker). Furthermore, mechanisms that allow end-user
intervention in the design and generation phase (e.g. cooperative R&D and advisory groups) not
only facilitate communication but they also improve the relevance of the generated technology.
In addition to costs and effectiveness considerations, various other factors influence the choice of
transfer mode, including:
* technology transfer infrastructure;
* incentive and value system of the organization;
Organizations with well-established technology transfer traditions and infrastructure (e.g.
extension system) are better suited for active delivery. The value system of the organization is also
an important determinant of the choice of delivery mechanism. For example, organizations
emphasizing proof of concept and publication in disciplinary journals are likely to opt for passive
information dissemination modes. Finally, institutions can be instrumental in the choice of
delivery mode. For example, existence and enforcement of intellectual property laws open up
modes of delivery otherwise not available (e.g. licensing).
The above model explicitly recognizes simultaneous influences of demand-pull and science-push
forces and other complex selection mechanisms on the direction of innovation and technology
transfer, It further illustrates how particular structural characteristics of both end-user and the
transfer organization act to influence the direction of innovation and technology transfer. Hence,
it achieves the primary objective of tying the structure of the technology transfer system to that
of the end-user.
The proposed model, however, is limited in an important way. Being structured around empirical
findings and stylized facts, it is a descriptive rather than a predictive model. It can effectively
rationalize observed behavior but it provides little guidance with respect to the response of any
one of the two transacting parties: the technology transfer organization and the end-user. Thus,
- a consistent theoretical framework that guides human behavior is necessary.
A Transaction Costs Approach To Agricultural Innovation and Technology Transfer
The overall innovation and technology transfer process can be viewed as a multitude of inter-
connected transactions. Such transactions are costly and, as it is posed here, the transaction
costs are substantial due to'intrinsic characteristics of the innovation and technology transfer
process. The transacting parties are assumed to minimizðe sum of transaction and
production (or transformation) costs. It is posed, however, that because transaction costs are a
large portion of the sum and are often incurred directly by individuals rather the overall
organization, such costs tend to dominate individual behavior. As a result, transaction costs
have significant impacts on the direction of agricultural innovation and technology transfer.
Transaction Costs Fundamentals: Understanding of transaction costs economics has been
enhanced in recent years through the works of Coase, North, Williamson and others. At the
heart of transaction theory lies the realization that it is costly to transact, and, that transaction
costs are important determinants of the organization and overall level of economic activity
(Coase), The costliness of information is the key to the costs of transacting (North).
Transaction costs involve the costs of measuring performance and the costs of protecting rights
and enforcing agreements in any particular exchange.
An important aspect of transaction costs is that they vary with the nature of transaction and the
way it is organized. The following transaction attributes are of importance in determining the
level of transaction costs (Milgrom and Roberts):
The specificity of the investment required to carry out a particular transaction, The
more specific the investment is the larger transaction costs will be as those making the
investment attempt to protect themselves from exposure to opportunistic behavior.
* The frequency with which transactions occur. Repeated transactions imply ability to
reward faithful partnerships and reduce the need for costly performance measurement
and monitoring mechanisms that safeguard rights. Therefore, transaction costs are
reduced with increased frequency of transactions.
The complexity of the transaction and the uncertainty about performance standards.
When uncertainty and complexity of a transaction make it difficult to predict the
performance that will be desirable, the costs of performance measurement, monitoring
and enforcement tend to increase. Hence, transaction costs increase with the complexity
of a transaction.
The difficulty in performance measurement. Even when the desired performance of an
exchange is predictable, it may still be difficult to measure such performance. Under
such circumstances, transaction costs tend to increase.
The connectedness of the transaction to other separate transactions. When performance
of one transaction is connected to another transaction, coordination mechanisms must
be establish to ensure performance. As a result transactions costs tend to increase.
Transaction Costs in Technolovy Transfer: Against this background, it is now possible to
assess the relevance of the basic proposition posed earlier that transaction costs in agricultural
innovation and technology transfer are large and are primary determinants of individual.
Intrinsic characteristics of innovation and technology transfer in agriculture that lead to large
transaction costs are:
* The tacit dimension of technical knowledge;
* the complexity of transaction and uncertainty about desirable performance of new
* the complexity of accurate performance measurement in technology transfer;
* loosely defined property rights.
As previously discussed, the tacit nature of technical knowledge is a primary cause of
asymmetric information between a "source" and an "end-user." Search, coordination, and
training costs may be expended by the "end-user" in an attempt to reduce informational
incompleteness. Training costs are most typical. Workshops, seminars, personnel transfers are
examples of activities directed towards passing tacit information on to the end-user. Such
transaction costs can be significant and increase with the complexity of the innovation that is
Inherent complexities and uncertainties of innovative activity make predictions about desirable
performance difficult and add to transaction costs. As an example of such costs consider the
case of genetically engineered pest-resistant plants. In addition to the scientific complexities of
achieving such traits in commercial lines, many additional inherent complexities and
uncertainties exist with regard to desirable performance. Questions about food safety and
impacts on the environment represent such complexities and uncertainties. The costs of
conducting experiments to assess such impacts and running relevant regulatory agencies are
part of the ttiasactions costs involved in reducing uncertainties and arriving at acceptable and
measurable desired performance. Clearly, such transaction costs are quite -large. Insurance
costs, contracts and other instruments that may be used to reduce liability in the case of
unfortunate unexpected effects from such new technologies increase transaction costs even
Performance in agricultural innovation and technology transfer is often hard to measure as it
may be difficult to decouple the contribution of technology from external random influences.
For example, the ability of a farmer to assess whether poor seed performance is due to
inferior genetics or bad weather, is often limited. Similarly, it is difficult for management to
immediately assess whether poor results in a given R&D project are due to limited effort
expended by a researcher or due to boundaries of the physical world. Under such
circumstances, resources must be expended to monitor and evaluate performance. Measuring,
monitoring and enforcing agreed upon performance among transacting parties becomes costly
. adding to the transaction costs of innovation and technology transfer.
Finally, for most agricultural innovations property rights are loosely defined. Recent efforts to
increase Intellectual property right coverage of agricultural innovations through plant breeders'
rights and utility patents are directed towards encouraging innovation by allowing some of the
innovation rents to be captured by the innovators through limited exclusive property rights.
Even under such conditions, however, the transaction costs associated with securing,
monitoring and enforcing such rights are quite large. Examples of such costs include invention
evaluation expenditures, patent application and maintenance fees, and monitoring and
infringement litigation expenditures.
After establishing that transaction costs in agricultural innovation and technology transfer are
large, the second basic proposition that transaction costs tend to dominate individual behavior,
needs to be evaluated. This is done here by means of an example. In particular, the behavioral
assumption of transaction costs minimization is used to rationalize recent trends in agricultural
Individual Behavior and Transaction Costs: In recent years, agricultural production has
become increasingly science-based. New technologies that have only recently began to enter
the market or await introduction promise that such trends will accelerate in the future. As
technology in agricultural production has become progressively more sophisticated, farmers
have been forced to search for increasingly complex and diffused information on technology
and business strategies. At the same time, the ability of the traditional extension agent to keep
up with such information base has been limited. As a result, the traditional delivery mode of
Land Grant Universities has become obsolete primarily due to its own success in transforming
Within this environment, it has become progressively difficult to assess the contribution of the
technical knowledge generated by the Land Grant Universities, Complexity of science and
connectness with R&D activities carried out by the private sector, obscure the contribution of
technical knowledge generated by the public agricultural research system which is embodied in
much of the technology used in agricultural production. Such difficulties have worsened as
technology transfer has been progressively "de-htunanized" by the gradual withdrawal of the
Because of such difficulties, the agricultural research and technology transfer system has faced
the difficult task of effectively demonstrating the value of agricultural R&D in recent years.
While several studies have estimated returns to agricultural R&D investment to be quite high
(Huffman and Evenson), they have had a limited impact in raising public support for
agricultural research and technology transfer. Under such conditions, performance
measurement costs and monitoring costs incurred by research administrators increased
drastically. When performance is too costly to monitor and measure, there exists no effective
incentive structure. Thus, large measurement and monitoring costs tend to decrease the ability
to manage, a rather unwelcome development by managers and administrators.
At about the same period of time, a general re-structuring of public U.S. universities brought
about by exogenous pressures for greater efficiency and accountability. With such added
pressures administrators turned their attention to standardized, more measurable academic
outputs such as publications in academic journals and grants and contracts. With emphasis on
such measurable outputs, performance measurement, monitoring and enforcement costs can be
substantially reduced. These developments re-directed the attention of individual researchers
away from applied research and more towards basic research conducive to publication in
disciplinary journals and grantsmanship.
This turn in emphasis would have probably happened even in the absence of probing from the
administration since a different type of transaction costs have been pushing individual
agricultural researchers away from applied-type research. While still charged with the
founding principles of Land Grant Universities, agricultural researchers have been receiving
weakening demand-pull signals as their traditional link with the farming sector -the extension
agent-- has been breaking away. Under such circumstances, the search and coordination costs
for establishing and maintaining communication channels with clientele groups so that research
priorities are in line with their needs, increased at substantial levels, Since search costs for
setting research priorities are far lower in the case of disciplinary and basic research, whose
priorities are typically better understood by individual researchers, a turn in agricultural
research emphasis was rather inevitable.
The pressures that have created these trends are not likely to ease up. In the absence of a
restructured outreach function that reduces the search costs for information to individual
researchers, emphasis on basic research will likely to continue.
The reduced role of extension and the increased needs for information by farmers has allowed
increased opportunities for private technology transfer, Most opportunities have been created
in the provision of specialized technical knowledge and services. Increasingly, such knowledge
is integrated with inputs and services into packages that reduce transaction costs incurred by
the farmer. For example, sales of fertilizer and seed are packaged with soil analysis services
and on-site expert advise. Similarly, improved genetics, feed rations, building technology and
on-site consulting have been packaged in hog contracts that have become increasingly popular
(Rhodes). All zuch technology packages tend to reduce search costs (e.g. costs of collecting
information on individual technology performance) incurred by farmers. They also reduce
measurement and monitoring costs since it is far easier to monitor and measure performance of
a total package rather than a set of complex individual components whose performance is
difficult to isolate. As agricultural production continues to become more science-based and
complex, the demand for packaged technologies is likely to increase.
Interactions between research administrators, researchers, farmers, extension agents, private
consultants, input suppliers, and other parties provide examples of the multitude of inter-
related transactions that define the process of agricultural innovation and technology transfer.
The dynamics of such interactions are described by the proposed non-linear innovation and
technology transfer model. Incorporation of the behavioral assumption of transaction costs
- minimization, allows such interactions between transacting parties to play out with solid
theoretical guidance. Under such conditions, it becomes possible to analyze the influence of
structural changes and exogenous factors on the structure of a technology transfer system and
ultimately on the direction of innovation and transferred technology.
We have argued that continuing use of the rational model of agricultural innovation and
technology transfer can provide limited insights on the structure of the technology transfer
system and the direction of innovation. Richer non-linear innovation models which endogenize
the process of innovation and technology transfer should be emphasized instead. We have
discussed one such non-linear model structured around existing empirical evidence and stylized
It was further argued, however, that utilization of non-linear innovation and technology
transfer models is only one step in the right direction. Such models need to be operationalized
through micro-foundation and consistent behavioral rules. We have argued that due to intrinsic
features of the innovation and technology transfer process, minimization of transaction costs
provides a satisfactory behavioral foundation that can be used to operationalize non-linear
innovation models, including the one proposed in this study.