ON-FARM RESEARCH: ORGANIZED COMMUNITY ADAPTATION AND LEARNING
FOR EFFICIENT AGRICULTURAL TECHNOLOGY INNOVATION
Professor, Food and Resource Economics Department University of Florida
Gainesville, Florida 32611 USA
Prepared for presentation at the
International Multiple Cropping Systems Conference
Jiangsu Academy of Agricultural Sciences
Cosponsored by the Chinese Academy of Agricultural Sciences October 8-11, 1985, Nanjing, Peoples' Republic of China
ON-FARM RESEARCH: ORGANIZED COMMUNITY ADAPTATION AND LEARNING
FOR EFFICIENT AGRICULTURAL TECHNOLOGY INNOVATION Peter E. Hildebrand
Professor, Food and Resource Economics Department University of Florida
Gainesville, Florida 32611
Thirty years ago rural sociologists in the United States talked about innovators, early adopters, late adopters, and non-adopters of improved agricultural technology (Bohlen and Beal, 1957; Rogers, 1962). Innovators were the ones who adapted technology to community conditions and they, along with early adopters, tended to be the primary beneficiaries of this technology. Late adopters benefited very little while non-adopters usually were affected negatively and were considered as laggards who failed to recognize the benefits of modernizing by adopting Improved technology. Researchers and extension agents studied the characteristics of the Innovators and early adopters, and tended to translate these characteristics Into model users of the products and services of agricultural research and extension institutions. This philosophy has directed technology development more and more toward Innovators and early adopters. At the same time, many who were late adopters and non-adopters have left farming and been absorbed into Industrial and urban life.
The environment for agricultural development in the United States in
the 1950s and 1960s created the "pro-innovation bias" (Rogers, 1983). This bias was based on the thought that new technology Is good and should be adopted. The fact that new technology might not be adoptable by all
farmers was seldom considered. That even the "better" farmers might need to adapt the new technologies to their specific conditions was given some consideration. But emphasis was placed on developing "Improved" technology and then selling or diffusing the technology to "enlightened", "1successful" or "good" farmers.
The purpose of this paper is to argue that the methodologies of agricultural, technology development and dissemination through on-farm research have now advanced to the point that they can be blended into a highly efficient process that serves all farming systems In a community simultaneously. These methologies can improve the social distribution of the benefits from public investment in agricultural research and extension and at the same time improve the efficiency of these activities.
Community adaptation, the adaptation or adjustment of a new technology to the agro-socloeconomic conditions of a community, is what Rogers (1983) calls "reinvention". In the past, adaptation of technology depended upon those farmers with better resources and management skills. This process resulted in technology better adapted to the resource and management conditions found on the farms of those innovators and early adopters. Late adopters, those farmers who had management and other resources somewhat different from the Innovators and early adopters, were forced Into additional adaptation of the technology to make It suitable for their less favorable situations. Non-adopters were comprised of those whose management and other resources were sufficiently different that the technology could not be adapted to meet their conditions. Many high yielding varieties CHYVs) of the Green Revolution of the 1960s and 1970s,
which produce superior yields on good soils with abundant moisture, have been disappointing, or even produce less than traditional varieties on infertile or arid soils. Modification of farm conditions to meet the requirements of the HYVs could not be achieved on many farms and hence those farmers became the non-adopters and did not benefit from this new technology.
At the same time that community adaptation of technology Is In
progress, the community is also becoming familiar with or learning about the new technology. This learning takes two forms. One is the hands-on or experiential learning of the Innovators or others using the technology.
servatlon and other forms of study by those who are not involved actively in the use of the technology (Wak e, 1984). A learning curves as Illustrated In Figure 1, relates achievements toward reaching potential results (such as potential yield from a new technology) with numbers of attempts at using, or experience with, the technology.
During the process of community adaptation, adjustments in the
technology (such as choosing a subset of components, modifying levels of Inputs or making the technology more nearly conform to community traditions) have the effect of facilitating learning. Facilitated learning, making a new technology more familiar, simpler, or easier to learn to use, shifts the learning curve to the left, Fig. 1. The ultimate potential of the technology so adapted may be lower than for the "maximum yield" or full technological package as shown In the figure. The opposite can also be true, but probably In fewer cases.
OTHER PWOten01 as ape
LeorVign curve for adapted/ Learning curve for sclentifle Package
observtion ig. 1. Sqiaet hift inth learning curve toromlft
Mo ahivee n aort t eido iehe learning curve occr with exerenebu
compagrbe moemrreent n ocurtruleaorng byil obsureran If ah fare
could be that of a later adopter who learned through observation, the equivalent of what the early adopter learned with one attempt at using the technology.
FARMING SYSTEMS RESEARCH AND EXTENSION
The Farming Systems Research and Extension (FSR/E) approach to technology development (see, for example: Gilbert et al., 1980; Shaner et al.,
RESULT a~~ljjo Led
/Learning uv for
1shift from later adopters
!/.L nircurve for
S Jearler adopters
(ao te onnoloqy)
NUMBER OF ATTEMPTS AT USING TECHNOLOGY Fig. 2. Shift in learning curve from observation before using the
1982; Hildebrand and Waugh, 1983) Is a means of formalizing community learning and adaptation. To a community, FSR/E brings the additional resources of outside scientific knowledge and expertise. By combining the efforts of scientists from several disciplines with those of the farmers in the community, adaptation to local conditions Is accelerated and dissemination Is more rapid.
In the FSR/E approach, much of the research required for technology adaptation Is conducted on farms, under real farm conditions, and with farmer collaboration. In the process, some farmers are helping adapt the technology while they obtain experience with It, Fig. 1. Others, those not directly Involved In on-farm testing, have the opportunity to learn by observation, Fig. 2.
RESEARCH DOMAINS AND RECOMMENDATION DOMAINS
in farming systems literature, recommendation domains (Byerlee et at., 1982) are comprised of farms and/or farmers with roughly homogeneous farming systems (Hildebrand, 1982). This Is similar to what Rogers (1983). for dissemination purposes, calls "homophiles". Reconnaissance surveys or sondeos are conducted to help define and delimit such recommendation domains. Refining or partitioning recommendation domains, as data from onf arm research is gathered and analyzed, has also been discussed (Hildebrand and Poey, 1985).
it is more meaningful and more useful to consider the concept of a research domain which is comprised of one or more recommendation domains. A research domain, similar to what Byerlee et al. (1980) call a "1homoqeneous target region", Is an ex-ante designation of a roughlv homogeneous agro-climatic zone throughout which it could be expected that a set of potential Interventions (technologies) could have applicability. For most technologies "the same experimental program may be Implemented over the whole region" (Byerlee et al., 1980, p 61). In many cases all, or nearly all farms in the target region area will pertain to the research domain n. An example Is a new disease resistant variety of a crop which would be expected to demonstrate resistance throughout the area. An exception would be with a new Implement for large tractors in an area where some farmers have large tractors and other farmers have no tractors. In this case the research domain for the Implement would include only those farmers In the area with large tractors.
In a research domain, alternative new technologies evolve into
treatments to be Included In experiments and trials for station research and/or on-farm experimentation. If research conducted in the research
domain involves several locations and Is designed to take advange of modified stability analysis (Hildebrand, 1984), then the biological research can result in the definition within the research domain of two or more recommendation domains based on biological response to the treatments, Fig 3. Other agro-socloeconomic research conducted simultaneously with the biological research (for example, directed agro-socioeconomic surveys, soil surveys, or farm enterprise budgets) provides Information needed to characterize the farms in each recommendation domain, so they can be Identified for further research and/or dissemination. These data are also valuable for second generation research Into cause-effect relationships which will help to explain results of first generation research (Swanberg).
YIELD RANGE OF e IN RESEARCH DOMAINS
RANGE OF IN
RANGE OF eIN
Fig. 3. Use of Modified Stability. Analysis to partition Research Domain Into Recommendation, DomalIns
FSR/E IN A COMMUNITY CONTEXT
To the extent that a community falls within a research domain, or that a research domain can be considered as a community, FSR/E becomes an organized and structured community learning and adaptation system for
agricultural technology Innovation. As an organized system and using the methodology described above, FSR/E is highly efficient In enhancing technology innovation In agriculture. First, because modified stability analysis benefits from the utilization of a wide range of environments, farmers who were formerly thought of as "innovators", "early adopters", "late adopters" and "non-adopters" can, and should all be included in onfarm research. improved regression estimates of the response of technologies to environments in modified stability analysis results from Including a wide range of farmers. This can Improve the efficiency of technology innovation for the superior environments (environments of the innovators) while at the same time providing adapted technology for late adopters and non-adopters in poorer environments. Hence, community
1- +-king rlar, q1militaneourtv for all strata of the communltv. In contrast to results of adaptation only by innovators, learning curve shifts resulting from adaptation in an FSR/E approach will benefit farmers in both favorable and unfavorable environments.
Both experiential learning and learning from observation are also
distributed more widely In the community from an FSR/E approach than from spontaneous community adaptation of centrally developed technology. When adaptive research or experimentation Is being conducted by a community Innovator, poorer farmers In the community may well be reluctant to obtain Information from the Innovator. By conducting on-farm trials over a wide range of environments in a community, FSR/E facilitates the process of obtaining information and of receiving hands-on experience with a technology. In turn, the social distribution of benefits is more equitable. Particularly relevant to the efficiency inherent In FSR/E is that shifts to the left in the experiential learning curve from adaptation
(Fig. 1) and from learning by observation (Fig. 2) can be cumulative. While a farmer is gaining Information about a technology, the technology can also be In the process of adaptation to community conditions. In summary, FSRIE complements, and makes more efficient, a naturally occurring technology innovatilon process In agriculture - community learning and adaptation.
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