Diffusion of Information in Agriculture in Senegal

The Case of Integrated Production and Pest Management

Diploma Thesis 2005 123 Pages

Economics - Case Scenarios



List of Figures

List of Tables

List of Abbreviations


1 Background of the study
1.1 Agricultural extension – from traditional top-down models to the participatory extension approach
1.1.1 History and role of agricultural extension
1.1.2 The Transfer of Technology model
1.1.3 The Participatory Extension Approach
1.2 The Farmer Field School concept of the FAO
1.2.1 The origins
1.2.2 The concept of FFS
1.2.3 Advantages and benefits of IPM-FFS
1.2.4 Problems and criticism
1.2.5 IPPM-FFS in Senegal
1.3 Objective of the study

2 Conceptual framework
2.1 Diffusion theory
2.1.1 Definition – Diffusion and Adoption
2.1.2 Diffusion of innovations Rate of adoption Rate of awareness and innovation-decision period Critical mass
2.1.3 Diffusion research
2.2 Social Cognitive Theory
2.2.1 Basic assumption
2.2.2 Information and knowledge
2.2.3 What can informal interaction achieve?
2.2.4 The value of information
2.3 Theory of Cognitive Dissonance
2.3.1 General description
2.3.2 Social pressure
2.3.3 Heterogeneity of population
2.4 Hypotheses of the study
2.4.1 FFS intensity and information diffusion
2.4.2 Information and stage of adoption
2.4.3 Transformation of FFS intensity into individual exposure
2.4.4 Specification of the survey hypothesis

3 Data collection and methodology
3.1 Sampling procedure
3.2 Questionnaire
3.3 General description of the study site
3.4 Methodology of analysis
3.4.1 Social Network Analysis Respect and Advice Network IPPM Talks Network
3.4.2 The logistic regression model The specification of the logistic model Estimation of the logistic function General evaluation of the model Evaluation of independent variables

4 Results of the survey
4.1 Socio-economic and instituional conditions in the study area
4.1.1 Demographic conditions
4.1.2 Social conditions Respect and Advice Network IPPM Talks Network Summary of results
4.1.3 FFS intensity and individual exposure Rationale of individual exposure The intensity and time of exposure
4.2 Diffusion of information about IPPM
4.2.1 The likelihood of receiving IPPM-related information
4.2.2 Cognitive dissonance and search for information
4.2.3 The intensity of information reception Quality of Information Quantity of Information Regression results
4.2.4 Summary
4.3 Effects of information diffusion on adoption behavior
4.4 Summary of the results

5 Conclusions and recommendations
5.1 Conclusions drawn concerning the diffusion of FFS-acquired knowledge
5.1.1 „What“ vs. „How“
5.1.2 Diffusion of IPPM knowledge in Senegal
5.2 Qualitative Data and experiences in the field
5.2.1 Problems to the implementation of IPPM Lack of water Limited or no credit supply Problems of commercialization and conservation
5.2.2 Incentives to participate
5.3 Implications of survey findings for the design and implementation of FFS-IPPM projects
5.4 Outlook

6 References

7 Appendices

List of Figures

Figure 1: The Transfer of Technology model

Figure 2: The participatory extension approach

Figure 3: The participatory extension approach cycle

Figure 4: Distribution of adopters and their categorization on the basis of innovativeness

Figure 5: The rate of awareness-knowledge and the innovation-decision period

Figure 6: The rate of adoption for usual and interactive innovations – the impact of critical mass

Figure 7: Variables Determining the Rate of Adoption

Figure 8: Relations between the three classes of determinants in the reciprocal causation

Figure 9: Knowledge, environment and their interface

Figure 10: Relation between magnitude of dissonance and active seeking of new information

Figure 11: The increase in exposure and adoption

Figure 12: The adoption process

Figure 13: Schematical presentation of the theoretical design of the study

Figure 14: Two villages as two points in time on the path of diffusion (rate of adoption)

Figure 15: Schematic structure of the villages

Figure 16: Region de Niayes (velvet) and the survey area

Figure 17: Distribution of the logistic function

Figure 18: Education distribution in the villages

Figure 19: Education and gender

Figure 20: Land tenure and gender in both villages

Figure 21: Average farm size owned by men/women

Figure 22: Right of decision and gender

Figure 23: Sociometric status for Gollam

Figure 24: Sociometric status for Keur Abdou Ndoye

Figure 25: Closeness index for Gollam

Figure 26: Closeness index for Keur Abdou Ndoye

Figure 27: Distribution of FFS farmers within the villages

Figure 28: Frequency of time of exposure in years

Figure 29: FFS intensity and rate of awareness

Figure 30: Number of talks about IPPM in the last month

Figure 31: Attitude of exposed farmers towards IPPM

Figure 32: Constraints to the diffusion of IPPM knowledge

Figure 33: Diffusion of IPPM to Non-FFS farmers in Senegal

List of Tables

Table 1: Comparison of “transfer of technology” and “participatory extension”

Table 2: Examples of immediate and developmental impacts of IPM-FFS

Table 3: Key characteristics of visited villages

Table 4: Sample characteristics in Gollam and KAN

Table 5: Land tenure issues

Table 6: Social network characteristics of the Respect and Advice Network

Table 7: Social network characteristics for IPPM Talks Network

Table 8: The shares of FFS, Exposed and Non-Exposed farmer groups by village

Table 9: Variables of individual exposure

Table 10: Cases of the dependent variable used for the analysis

Table 11: Explanatory variables used for the analysis

Table 12: Expected signs of the regression coefficients of the logistic model

Table 13: Results of the regression

Table 14: Initiator of the talks

Table 15: Logistic regression results. Dependent variable: Passive exposure
or active search for information?

Table 16: Quantity of information transfer

Table 17: OLS-estimation results. Dependent variable: “Number of persons talked with
about IPPM”

Table 18: OLS-estimation results. Dependent variable: “Frequency of IPPM-related talks
in the last month”

Table 19: OLS regression results. The dependent variable is “IPPM is a solution to
prevailing agricultural problems”

Table 20: Results of the binary logistic regression. Dependent variable: “wish to adopt”

Table 22: Diffusion of IPPM knowledge to Non-FFS farmers

List of Abbreviations

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Using participatory training approaches such as farmer field schools (FFS) is perceived to be an appropriate technique to improve farmers’ knowledge of complex agro-ecological systems and Integrated Production and Pest Management (IPPM) technologies. Through intensive and exploratory learning farmers are trained to make competent and adequate decisions adapted to their local, specific environment. However, the FFS approach appears to be more costly than alternative less intensive approaches of knowledge transfer. Calculations of the costs per farmer trained in Indonesia and the Philippines amount to US$ 45 to 60, which are considered to be rather underestimates of the true costs. These high per capita expenditures are more justified, if knowledge can be disseminated through informal farmer-to-farmer interactions. Participatory extension approaches therefore rely on interpersonal channels and group mechanisms for diffusing greater awareness and facilitating learning among the group of untrained farmers. Although diffusion is not an explicit goal of the FFS approach, it is nevertheless a desired side-effect, which could invalidate the reproach of fiscal unsustainability.

However, several empirical studies found that knowledge generated by participatory extension training does not always sufficiently diffuse to non-participating farmers, because of the complexity of knowledge imparted in the course of a FFS. But even though there seems to be little diffusion of knowledge, farmers nevertheless exchange their experiences with each other, and thus, information about IPPM is being disseminated in the village. This diffusion of information can have a significant impact on adoption behavior and be an important factor for a successful introduction and establishment of an innovation like IPPM. Depending on the project placement strategy, diffusion of information can be boosted and promoted, or it can be hampered by creating unfavorable conditions.

This case study, conducted in the‘ Région de Niayes ’in Senegal in 2004, investigates the effects of training intensity on the diffusion of information. A total of 341 vegetable growers were interviewed in two villages (Gollam and Keur Abdou Ndoye) that had different shares of farmers trained in IPPM-FFS (Gollam: 3% - Keur Abdou Ndoye: 14%) but are similar in all other respects. The objective of the study was to identify the factors determining the intensity of information diffusion. A set of predominantly closed questions was used to generate data on demographic, farm-, IPPM-, and information-related issues, which are considered important to capture the diffusion processes. The data has been analysed using a logistic regression model as well as the ordinary least-square (OLS) -estimation model.

The results show that the proportion of trained farmers affects the dissemination of IPPM-related information. A higher share of FFS participants increases the individual exposure of non-participants. As a result, the likelihood of receiving information about IPPM is four times higher in Keur Abdou Ndoye than in Gollam. Consequently, the number of exposed farmers approaches 100% in Keur Abdou Ndoye. In addition, the quantity of information that is shared is higher. The individual exposure to IPPM has a statistically significant positive impact on information sharing. A second level of impact is the stage of adoption of FFS farmers and the observability of IPPM practices and benefits, which is a crucial variable for the intrinsic motivation of exposed farmers to adopt such technologies.

1 Background of the study

1.1 Agricultural extension – from traditional top-down models to the participatory extension approach

1.1.1 History and role of agricultural extension

Poverty and hunger, food production and natural resource degradation are the great challenges in today’s world. The most affected and vulnerable group is the rural population of developing countries. More than 80% of the poor live in rural areas. In the face of a rising population in the LDCs[1] (2.4% - annual growth rate 2000-2002)[2], improving agricultural productivity and farmers’ incomes are important issues in the fight against poverty, which are addressed by all involved governments and organizations of development assistance.

Agricultural extension is a tool, that can supply improved skills, information and ideas to people involved in the agricultural sector in order to develop an agriculture that will meet complex demand patterns, reduce poverty, and preserve ecological resources.[3]

The term “extension” originates in the discussion of the Oxford and Cambridge universities about how to serve the educational needs of the growing population in the urban area in the 19th century. The movement that emerged out of this discussion was designated “university extension”[4]. The success of this work in Britain initiated similar activity elsewhere. Since the 19th century and the potato famine attempts were developed in many European countries to impart useful knowledge to farmers through itinerant agricultural teachers or in other forms.[5] This significant social innovation and important force in agricultural change, has then been created and recreated, adapted and developed over the centuries. Particularly in the last decades extension operations “may well be the largest institutional development effort the world has ever known. Hundreds of thousands of technicians have been trained and hundreds of millions of farmers have had contact with and likely benefited from extension services.”[6]

Today we understand agricultural extension as both, a system (institutional interpretation), and a set of functions (functional interpretation) performed by that system to induce voluntary change among rural people. It is largely publicly funded (80 percent of the world’s extension services), as most of the aspects of agricultural knowledge supply have the nature of public goods.[7] Worldwide agricultural extension employs more than 800.000 extension workers. Especially the developing country governments invested largely, as they expect an increased agricultural production induced by the information input provided by extension efforts. “Between 1959 and 1980, spending in real terms for extension grew more than six-fold in Latin America, tripled in Asia, and more than doubled in Africa.”[8]

The goals of extension are transferring knowledge from researchers to farmers, advising farmers in their decision-making, educating farmers to be able to make similar decisions in the future, enabling them to clarify their own goals and possibilities and to fulfill them, and stimulating desirable agricultural developments.[9]

1.1.2 The Transfer of Technology model

Until recently extension consisted mainly of farmers and communities being instructed what to do, often by institutions that had not carefully and thoroughly identified their local needs. The Transfer of Technology model (ToT), a rather “engineering” approach was the prevalent practice for developing and spreading of innovations. It bases on the assumption that gaps between the actual productivity of the farms and the potential productivity with better technology and know-how could be bridged if farmers had better access to certain inputs and used them according to a set of prescribed instructions. Extension would thus reduce the differential between potential and actual yields in farmers’ fields by accelerating technology transfer (reducing the technology gap) and helping farmers to become better farm managers (reducing the management gap)[10]. In this view farmers are often considered as the main constraint to development, as mis-managers of their resources. The role of the extension agent is to assist farmers in putting the ready-made technology into practice, thus running the danger that it may not be appropriate.[11]

The results tended to be poor. The ToT-model based on a one-way communication that discouraged feedback of information. Researches worked independently of farmers and extension workers. The “innovations” were developed on research stations without considering the on-farm problems of the farmers. The dissemination of these innovations and technologies relied then on the extension workers. They were seen as technical agents. Social competence was not required as the socio-organisational issues were neglected or reduced to a technical level. In short, this linear conception viewed farmers, extensionists and researchers as three separate strata and the links between them have been weak or non-existent (Figure 1).

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Figure 1: The Transfer of Technology model

Source: Semana (2002), p. 3

The top-heavy ToT model led to highly bureaucratic structures with all their specific problems like lack of accountability[12], fiscal unsustainability and poor interaction with other stakeholders[13], and also implied low adoption rates, because farmers did not have any sense of ownership of the ideas imposed on them.[14] The methods were technology-driven, not farmer-driven; they were centrally uniform, not locally adaptive.[15]

This top-down approach has been often criticized and a general change towards participatory approaches is sweeping through the development movement. The notion that “extension systems should be made more accountable to the clients and service delivery should be demand-pulled rather than supply-driven”[16] is more and more prevalent.

1.1.3 The Participatory Extension Approach

It is recognized that extension has a dual function. It not only does translate information from the researchers to farmers, it also has an important role in supporting researchers to tailor technology to the agro-ecological and resource conditions of farmers. At the heart of this change is the awareness that rural people themselves are the owners and shapers of their own development. The extension agent is no longer seen as the expert who has all the knowledge and technical solutions. The client’s own knowledge and ingenuity is seen as a major resource. Solutions to local problems are to be developed in partnership between research, extension agents and the rural population. This definition of the so-called participatory extension approach (PEA) implies three principles:

Participation: The process of technology identification, development and transfer must include the farmers in the locality being served.

Integration: The process of extension must involve researcher, extension agent and farmer in an integrative manner, using also local resources or personnel.

Practical relevance: Technology development and transfer must focus on actual and immediate problems of farmers.[17]

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Figure 2: The participatory extension approach

Source: Semana (2002), p. 5

The main failure of traditional extension models was the underestimation of the fact that outsiders are not able to truly determinate the “best practice” for specific local conditions. Farmers know their personal circumstances better and therefore are in a better position to decide about the management of their farms within the many environmental and social constraints they face. It is an illusion that outsiders can fully understand the totality of factors, which influence the behavior of local stakeholders. For successful innovation development and adoption, farmers need to experiment, adapt, evaluate and determine the practices most appropriate for their own situation. They need to become “experts in farming”[18], mastering the ecological principles and developing solutions themselves. Agricultural extension therefore has to be participatory, considering all the different interwoven social, economical, cultural, political and ecological factors that determine the working conditions of farmers.

Within this concept, the extension agent is seen as a “facilitator”, not as a “teacher” or “instructor”. Instead he plays the role of a mentor or educational companion. Both, facilitators and farmers become active participants in the educational process. The hierarchy between them is eliminated and a sense of community formed. Knowledge is created not transferred and is considered to be located in the community rather than in the individual.[19] Problems are identified and solutions are generated in collaboration between farmers and the facilitator. Table 1 summarizes the differences between the ToT and the PE approaches.

Table 1: Comparison of “transfer of technology” and “participatory extension”

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Source: Hagmann et al. (1998), p.5

As far as the operational implementation is concerned, the participatory extension process can be divided into four phases:

Phase 1: Social mobilisation: Facilitating the communities’ own analysis of their situation

Phase 2: Community-level action planning

Phase 3: Implementation and farmer experimentation

Phase 4: Monitoring the process through sharing experiences, ideas and self-evaluation

Figure 3 visualizes this interactive education process, as developed through experiences of extension work in Zimbabwe. The self-evaluation at the end of the first cycle in the process leads to the next cycle, which starts again with social mobilisation. PEA is a continuous process of learning.[20] The potentials and pitfalls of participatory extension will be discussed in the next chapter, in connection with the farmer field school approach.

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Figure 3: The participatory extension approach cycle

Source: Hagmann et al. (1998), p.15

1.2 The Farmer Field School concept of the FAO

1.2.1 The origins

Within the framework of participatory extension several methodologies or tools have been developed as for example group extension methods, farmer-to-farmer extensions, farmer field schools (FFS), master farmer training, diagnostic survey, farmer participatory research and rapid rural appraisal[21].

The FFS approach emerged in the late 1980s and was first implemented by the FAO in Indonesia as an answer to the poor performance and even the failure of precedent extension work, especially during the “green revolution” in East Asia from the 1960s to the 1990s. The long-term effects of the traditional engineering extension concept had been disastrous. The standardized “production packages” were not adaptable to local conditions. Inadequate and abusive use of pesticides caused dramatic losses to national food production and also aggravation of the financial situation of the farmers[22]. The estimated yield loss in Indonesia in the year 1976-1977 for example, caused by the brown planthopper, was 364,500 tons of rice – “enough to feed three million people for an entire year.”[23]

International Rice Research Institute (IRRI) researchers found in studies in the late 1980s that farmers had the capacity to learn, innovate and even outperform research stations in terms of average yield. If they only had the knowledge about new technologies, they could discriminate among the solutions offered to them by the research system, adapt the technologies to their particular environmental conditions, and provide supervision of inputs to ensure the appropriate application of the technology.[24] This acknowledgement of the indigenous potential of farmers led to a change of the existing extension systems.

1.2.2 The concept of FFS

The Farmer Field School is a group approach to agricultural technology development, focusing on adult, non-formal education through hands-on field discovery learning.[25] Originally designed as a way to introduce knowledge on Integrated Pest Management (IPM) to irrigated rice farmers in Asia, the FFS approach emphasises four principles:

i. Grow a healthy crop
ii. Conserve natural enemies of insect pests
iii. Monitor the fields regularly
iv. Become IPM experts through participation in FFS[26]

Operationally, the FFSs are organized around a series of 9-12 half-day long weekly sessions in a group of 20-25 farmers during a single crop season, focussing on biology, agronomic and management issues, where farmers conduct agro-ecosystem analysis, identify problems and then design, carry out, and interpret field experiments using IPM and Non-IPM comparisons.[27]

1.2.3 Advantages and benefits of IPM-FFS

This concept of learning and education is highly responsive to local needs over a wide range of conditions, and with a wide range of crops. It helps farmers to acquire an understanding of important “system” concepts and relationships. In addition FFSs also include a significant focus on group and individual capacity building.

A key feature of the FFS approach is that it rectifies the problem of accountability, traditional extension models were so often confronted with. This aspect is addressed in two ways:

i. The facilitators are bound by a strict timetable of sessions within a predefined curriculum that can be easily looked-over by supervisors.
ii. The continuous interaction with a cohesive group of farmers creates a certain accountability to the group, in particular when the facilitator is a farmer-trainer, who is member of the same community.

These characteristics of FFS are expected to ensure the quality of knowledge provided to the farmers.[28]

As mentioned above, the FFS approach was designed originally as a way to introduce knowledge on Integrated Pest Management. Although the farmer field school concept has been adopted and imitated by many official and non-governmental organizations, with different educational contents, the FFS programs conducted by the major player in this field, the FAO, are still designed to promote IPM.

The performance of the FFS (IPM) approach has been examined and evaluated by numerous Impact Assessment (IA) studies. Most of them concentrated on measuring immediate impacts like the effects on pesticide use and yield. The recent discussion however leads to a more complete assessment of the broad range of developmental impacts, including changes in the social and political domain. Table 2 resumes the possible impact dimensions of the farmer field schools.

Table 2: Examples of immediate and developmental impacts of IPM-FFS

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Source: van der Berg (2004), Chapter 2.2

In a meta-study on Impact Assessment van der Berg draws this recapitulatory conclusion on the impact of IPM-FFS throughout the world: “The majority of studies measured the immediate impact of training through aggregated data, and reported substantial and consistent reductions in pesticide use attributable to the effect of training. In a number of cases, there was also a convincing increase in yield due to training. (…) A number of studies described broader, developmental impacts of training often using qualitative methods, and in some cases involving farmers in identifying and describing the impacts. Results demonstrated remarkable, widespread and lasting developmental impacts, which have been best documented for Indonesia. It was found that the Farmer Field School stimulated continued learning, and that it strengthened social and political skills, which apparently triggered a range of local activities, relationships and policies related to improved agro-ecosystem management.”[29] Beyond these impact effects, recent studies emphasize also the significant environmental and health benefits of IPM-FFS, which is measured for example using the concept of the Environmental Impact Quotient (EIQ)[30]

1.2.4 Problems and criticism

However, participatory extension methods such as farmer field schools face the same problem that has always dogged large extension systems – fiscal unsustainability. The FFS and other PE approaches can be relatively expensive (if compared to media campaigns for example) – both in time and in related training costs – because they require farmers’ hands-on participation in small, trainer-facilitated groups. Calculations of the costs per farmer trained in Indonesia and the Philippines amount to US$ 45 to 60, which are considered to be rather underestimates of the true costs.[31] Therefore diffusion effects play an important role. PEAs rely heavily on interpersonal channels and group mechanisms for diffusing greater awareness and facilitating learning among the group of untrained farmers. Although diffusion is not an explicit goal of participatory extension approaches like FFS, it is nevertheless a desired side-effect, which could invalidate the reproach of fiscal unsustainability. Knowledge of IPM, disseminated to non-trained farmers, can uplift the general agro-economical performance of small-scale farmers. Economically spoken, it’s the positive external effects of FFS that ensure a better efficiency of the training. If certain contents and practices could be communicated and adopted without official expenses, the benefit-cost ratio would increase. This farmer-to-farmer extension could raise the program coverage, allow FFS-acquired knowledge to spread faster and thereby make the FFS a more cost-effective approach to agricultural extension.

The FFS approach relies on two knowledge transmission principles, in order to reduce the fiscal burden and to accelerate the diffusion of IPM. First, the training of farmer trainers (TOFT). FFS graduates are encouraged to undertake a TOFT, thus becoming facilitators themselves, and subsequently to train other farmers. Through TOFT a large number of facilitators can be trained, who know their local conditions well and who can conduct FFS in their own or in neighboring villages. Second, in addition to this formal diffusion mechanism the transmission of knowledge also works through informal farmer-to-farmer communication.

Several studies have been conducted to identify the diffusion of FFS-acquired knowledge. The empirical evidence has brought some criticism to the FFS approach, especially from the World Bank. A scientific debate over the fiscal sustainability of FFS is now going on since 4 or 5 years, which was set off by a study by Agnes C. Rola and Serlie B. Jamias (2002)[32], a second study by Gershon Feder, Rinku Murgai and Jaime Quizon (2002)[33], and a repeated study by Feder et al. in 2004[34]. All three studies questioned the diffusion effects that before had been assumed to multiply the knowledge that had been imparted on the FFS farmers.

These studies found that knowledge gained by farmers in a FFS “does not diffuse in a significant way to other members of their villages”[35], and “non-participants appear hardly to be affected”[36]. This again leads to the conclusion that “if the likelihood of farmer-to-farmer diffusion of FFS information is negligible, then the program will need to train directly large numbers of farmers, otherwise, it will have a very limited impact at the national level. But given the high costs associated with the intensive FFS training, the fiscal dimension becomes a serious obstacle as many countries would not be able to afford the large fiscal expenses, over a long period of time.”[37] An extrapolation of the training efforts of farmers who had undergone the TOFT in Indonesia shows furthermore that even the first diffusion effect “cannot be relied upon to maintain a significant training effort under the FFS approach.”[38]

The reasons for this “failure” could be the complexity of knowledge, imparted in the course of an FFS, as the training aims at developing the analytical skills, critical thinking, and creativity of farmers and to help them learn to make better decisions. FFS graduates do not master a specific set of contents or “messages”, they rather master a “process of learning”.[39] For this reason knowledge does not diffuse well to other village members without the exploratory activities that are the key part of the FFS approach itself.[40]

These findings were repeatedly challenged by other scholars[41]. Several letters have been sent to the authors of the mentioned study[42]. Workshops on FFS have taken up this issue and discussed and criticized the methodology and findings of the survey, sometimes in a rather polemic manner. Gallagher replies to Feder’s critique[43]: “The paper points out the obvious – that most extension services have no money to implement programs without external budgets. (…) Using their paper, one would also conclude that any public expenditure is too expensive. But governments still fund education, health and other services.” Other calculations seem to contradict or relativize the calculations by Feder et al. (2004) and Quizon et al. (2004). Owens and Simpson (2004) stated that the costs per farmer under the East African conditions vary between US$9 and 35, and in Ghana the estimated costs amount even to less than US$10[44]. It seems, however, that the view of the World Bank on the fiscal sustainability of the FFS has not changed. But even if researchers have reason to disagree with Feder’s conclusions concerning the fiscal sustainability of FFS, the World Bank studies have quite serious implications regarding the diffusion of knowledge through informal interaction.

1.2.5 IPPM-FFS in Senegal

In Africa, the FFS approach was first introduced by the FAO Global IPM Facility within the IPM-FFS program, in the year 1995 in Ghana. Since then 6.000 farmers and 400 extension agents have been trained in Ghana, covering a dozen different crop species.[45] Following the efforts in Ghana, a National IPM Program was established in Mali in 1998, which spread rapidly in the following years. At the same time similar efforts were initiated in Kenya and Zimbabwe. To date the Global IPM Facility has helped to start, or is currently working with FFS programs in over a dozen African countries, from Senegal to South Africa. Several of these have moved beyond the pilot stage and are expanding their activities.

In 1999, Senegal was selected as one of three pilot countries for a West African regional project on Integrated Production and Pest Management (IPPM). (The introduction of IPM FFS in Africa has shown that there are broader agronomic, management and production issues that have to be addressed by the facilitators. This has led the FAO to talk of IPPM rather than just IPM. IPPM goes beyond plant protection with links to irrigation and fertilizer information.)

Set up by the Global IPM Facility for the Food and Agriculture Organization of the United Nations the project is carried out by the Foundation CERES-Locustox[46]. The first phase of the main program started in 2000 and continued until the end of 2004, after which the program was to be continued for a second phase. The focus of the program was on smallholder vegetable growers because of their increasing reluctance to continue with existing pesticide practices, attendant risks of residues of unauthorized pesticides, accidents and the high costs resulting therefrom. In Senegal, pesticide use is highest among vegetable and cotton growers.[47] Beyond that a great anxiety prevails among farmers because of lack of sufficient irrigation water and increasing infertility of soil. The FFS curriculum took up some lessons on compost, organic manure and prevention measures against fast evaporation. The pilot phase of farmer training through farmer field schools was a success. 205 FFS have been conducted, with 166 on vegetables. Until now some 3700 farmers have been trained, and a preliminary Impact Assessment study based on the FFS experimental plots has shown encouraging results like positive yield effects and a significant replacement of chemical pesticides by biological pesticides through FFS IPPM training[48]. At the end of the first phase, the project has been evaluated by FAO staff and has been approved as worthwhile to be continued. In addition, a large cross-country Impact Assessment Study is being conducted by the University of Hanover in collaboration with the national coordinators of Senegal, Mali and Burkina Faso, and supported by the FAO, to examine the economic impact of FFS-IPPM training on the performance of small-scale rice and vegetable growers. For the second phase, which will continue for another four years, Cap Verde and Benin will join the project.[49]

1.3 Objective of the study

This study analyzes the implications of different approaches to the design and the implementation of IPPM-FFS for the process of diffusion. Most important is the question, how the proportion of trained farmers in a village influences the dissemination of knowledge or information. Designing an FFS project several approaches can be used as far as project placement is concerned. The object is generally always the same: to reach as many farmers as possible in a certain region. But the way of implementing can differ. On the one hand, the decision makers can follow a “drop-strategy”, conducting one FFS per village, thus introducing IPPM to as many villages as possible, but receiving a very small share of adopters per village. On the other hand, they may concentrate on view villages and continually support them over a longer period, ensuring a high adoption rate and a profound and intensive training of farmers. Both approaches have different implications for the diffusion of knowledge.

Two interdependent aspects can be identified in the process of diffusion. First, the spread of information or knowledge about IPPM and second, the actual adoption of IPPM. Both issues are interrelated as with more and more farmers adopting IPPM the intensity (quality and quantity) of information exchange increases. Contrarily, with an increasing village-wide attention to, and intense communication about, IPPM, the social forces that influence adoption increase, so that IPPM becomes a socially accepted and valued practice. At a certain adoption rate (share of adopters in the village), this process of mutual effects becomes self-sustaining and pushes adoption forward to an equilibrium point, or even up to 100%. This adoption rate is called “the critical mass”.

In order to evaluate the different implementation approaches, this study tries to investigate the flow of IPPM-related information in two villages with different adoption rates - one below and one above the hypothetical critical mass of around 10%.

The results of this study may open up better ways to exploit the potential of FFS training. They can possibly be used to make better decisions with regard to project placement, achieving a higher acceptability and stage of adoption of IPPM, and thus a more efficient use of resources in the second phase of the project.

The next chapters are organised as follows:

Section 2.1 contains a concise overview of diffusion theory, mainly based on the classical work by Everett M. Rogers[50]. In section 2.3 an exact definition and scope of the study, the general hypothesis, and several questions derived from it, will be given, which shall serve as a guideline in the analysis of the survey data.

Chapter 3 will then give an overview of data collection and the methodology used for analysis, particularly the logistic regression model.

The main part of this study, the results of the analysis, are presented in chapter 4, following the outline of hypotheses of section 2.3.

In Chapter 5 finally some conclusions and recommendations are given.

2 Conceptual framework

2.1 Diffusion theory

2.1.1 Definition – Diffusion and Adoption

Diffusion, as defined by Rogers, is a “process by which an innovation is communicated through certain channels over time among the members of a social system.“[51] This process consists essentially in the communication of a new idea, whether it occurs autonomously, irrespective of any intervention, or directed and managed.

In a broader view, diffusion can be seen as a process of personal and social change. It is a process by which alteration occurs in the structure and function of a social system. When new ideas are invented, diffused, and adopted (or rejected), leading to certain consequences, social change occurs.[52]

In the first sense, however, diffusion is the spread of information or technology. It is a desired effect, which accompanies every kind of intervention and is often the automatic multiplier of its impact, be it social or economical. Particularly in the field of extension service, diffusion is a core concept. There is a tremendous wealth of theoretical and empirical research on the diffusion of innovations.

For a better understanding, an exact definition of adoption and diffusion will be given: Adoption is the decision of a specific decision unit (individual, group, firm) to begin using a new technology. It is the culmination of the mental processes of shaping a positive opinion or attitude towards a new idea. From the point of first awareness the individual goes through a process of information processing and develops a persuasion that depends on individual characteristics, the environment, or the characteristics of the innovation, and leads finally to implementation (or rejection) of the new technology. Adoption studies are of a microeconomic nature and try to analyze and explain why at a given point in time, some individuals have adopted while others have not.[53] In contrast to adoption, diffusion is the “path of aggregate adoption”[54] by a multiplicity of decision units. Diffusion is a dynamic process that focuses on the penetration of a social system by an introduced innovation. Studies on diffusion are of a rather macroeconomic nature. They rely on aggregated data to explain differences in the rate of diffusion of different types of technologies or the same technology in different geographic areas.[55] This distinction is more practical than conceptual, since it is the microeconomic decisions by individuals that drive the diffusion of an innovation.

2.1.2 Diffusion of innovations Rate of adoption

The spread of an innovation usually follows a common pattern. Most of the psychosocial factors that affect the pace and pattern of diffusion are normally distributed, thus leading to a normal adoption distribution – a bell-shaped curve, when plotted over time on a frequency basis (see Figure 4). The differences in adoption behavior have led Rogers to a categorization of adopters using the criterion of “innovativeness”, “the degree to which an individual or other unit of adoption is relatively earlier in adopting new ideas than other members of a social system.”[56]

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Figure 4: Distribution of adopters and their categorization on the basis of innovativeness

Source: Rogers (2003), p.281

If plotted as a cumulative number or percentage of adopters, the result is the typical S-shaped diffusion path, or rate of adoption (see Figure 5). Initially, the rate of adoption is slow because new ways are unfamiliar, customs resist change, and results are uncertain. The curve then accelerates to a maximum until half of the individuals in the system have adopted. From then on the marginal increase of the adoption rate is diminishing. Rate of awareness and innovation-decision period

The shape of the diffusion curve, or the speed of the diffusion process, depends on two factors: the rate of awareness-knowledge and the innovation-decision period (see Figure 6). The first is the speed of information dissemination. A more rapid communication of information about a new idea leads to an earlier creation of knowledge. This leads to a left-shift of the rate of awareness-knowledge. The second, the innovation-decision period, is the time required for the individual decision process between first awareness of the innovation and the actual adoption. Supplying the individual with additional information and decision support can shorten the time of decision-making, or more general, the time of forming an opinion concerning the innovation. This again leads to a left-shift of the rate of adoption.[57] The result of both effects is the acceleration of the diffusion process.

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Figure 5: The rate of awareness-knowledge and the innovation-decision period

Source: Rogers (1995), p.200 Critical mass

As already discussed above, both processes - the spread of awareness and the adoption of a new technology - are reciprocal and influence each other. At a certain point (which varies depending on the social system or the nature of the innovation) these effects become self-sustained and the speed of diffusion accelerates enormously without any external influence. The critical mass is particularly relevant for the so-called interactive technologies (e.g. telephone, e-mail) where the utility of the innovation increases for all adopters in the course of diffusion. Not only do earlier adopters influence later adopters, but later adopters also influence earlier adopters in this process of reciprocal interdependence.

Critical mass technologies show a very low variance of individual thresholds, i.e. a great share of decision makers is willing to adopt when the critical mass has been reached (see Figure 6). An innovation like IPPM, where synergetic effects occur with an increasing rate of adoption, is certainly that kind of technology where the critical mass theory can be applied. Early adopters have still to put up with negative externalities from their farm neighbours using chemical pesticides, and there may be no developed market for “bio”-vegetables. With an increasing rate of adoption, the late as well as the early adopters benefit from each other in terms of pest control and better commercialisation possibilities.

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Figure 6: The rate of adoption for usual and interactive innovations – the impact of critical mass

Source: Rogers (2003), p.344

2.1.3 Diffusion research

Figures 4-6 summarize the basics of diffusion theory, which were repeatedly tested in empirical studies. The main phase of research started around the time of World War II. The reason was that very a rapid agricultural change began to take place in the US and Europe. Secondly, developing countries gained their independence and the shift from colonialism to development led to an intense interest in planned change.[58] The results of the studies had very practical implications for extension practice – the process became a strategy. Ex-post studies of historical situations in which diffusion processes had taken place were translated in the ex-ante expectation that diffusion would take place[59]. Later however, diffusion research came under attack, partly due to its lack of attention to structural variables (society) and its emphasis on socio-psychological (individual) ones[60], which has led to an erroneous comprehension of diffusion patterns. Particularly the categorization of adopters and the logical reasoning derived from it, that late adopters or “laggards” are ignorant, traditional or conservative, was to be revised. Farmers’ decisions to adopt or to reject an innovation are always made under consideration of their personal resource endowment as well as the social conditions they face. Often, well educated and even innovative farmers may decide not to adopt, if they cannot afford it, or the risk of failure appears to be to high. As Professor Chigozie Asiabaka put it: “The truth is that when farmers do not adopt they do so because they are wise, and not because they are ignorant!”[61].

Diffusion research focuses on a broad spectrum of variables and effects, which have been analyzed regarding their impact on the spread of innovations. Static adoption studies as well as diffusion studies, which depend on time series or panel data, identified a number of characteristics that have been proposed as determinants of adoption behavior. Apart from innovation-specific variables (trialability, relative advantage etc.) the characteristics of the individual or the environmental conditions affect the rate of adoption.[62] In more recent studies the importance of social effects such as density, homophily and interconnectedness of networks, the individual social integration and social capital, as well as the effects of social pressure on individual adoption decisions, is stressed.[63]

Figure 7 gives an exemplary list of the different types of variables that may play a role in the diffusion process.

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Figure 7: Variables Determining the Rate of Adoption

Source: Based on Rogers (2003), p.222 and Roeling (1988), p.82

In the following text, a short review of literature on diffusion of IPPM will be presented, to resume the state of scientific research.

Palis, Morin and Hossain (2002) investigated the influence of social networks on the diffusion of IPM knowledge. Their study showed that family relations and farm neighbourhood compose homophilous social clusters, which offer good conditions for spontaneous diffusion of IPM technology, because they offer learning by observation, day-to-day communication and face-to-face instruction possibilities. Comparing two villages, Palis et al. found, that the spatial distribution of participants had effects on the diffusion of knowledge. Whereas in one village the farms of trained farmers were located in a contiguous area, which confines or limits the sharing and learning of new technology among kin groups and non-kin owners within that area, in another village the farms of participating farmers were geographically distributed producing a fast and natural spread of FFS technology.

A study by Rola and Jamias (2002) which they conducted in the Philippines revealed that interpersonal networks appear to be the predominant method by which farmers acquire knowledge. However, the results showed that there are considerable differences in knowledge between FFS participants and exposed farmers, while no difference could be found between exposed and control group farmers, even though, farmers themselves claimed to share knowledge with others. Rola et al. concluded that the informal interactions between FFS graduates and Non-FFS farmers currently are not at a level that can be relied on to transfer pest management knowledge on a wide scale.


[1] Least Developed Country

[2] The Least Developed Countries Report 2004, p.321

[3] see Feder, Willett and Zijp (1998), p.1

[4] Jones and Garforth (1997), p.1

[5] see Jones and Garforth (1997), pp.1 and 5

[6] Anderson and Feder (2003), p.21

[7] see Fleischer, Waibel and Walter-Echols (2002), p.310

[8] Feder, Willett and Zijp (1998), p.4

[9] see Feder, Willett and Zijp (1998), p.3

[10] see Anderson and Feder (2003), p.3

[11] Hagmann, Chuma, Murwira and Connolly (1999), p3

[12] see Feder, Willett and Zijp (1998), p.9

[13] see Fleischer, Waibel and Walter-Echols (2002), p.309

[14] see Hagmann, Chuma, Murwira and Connolly (1999), p.1

[15] see Pontius, Dilts and Bartlett (2002), p.15

[16] Fleischer, Waibel and Walter-Echols (2002), p.310

[17] see Asiabaka (2001), p.2

[18] Pontius, Dilts and Bartlett (2002), p.16

[19] see Whipple (1987), p.3

[20] see Hagmann, Chuma, Murwira and Connolly (1999), p.6

[21] see Hagmann, Chuma, Murwira and Connolly (1999), p.2

[22] Pesticides can often be the cause for severe pest outbreaks, as they also diminish the population of natural enemies of certain pests.

[23] Pontius, Dilts and Bartlett (2002), p.14

[24] see Pontius, Dilts and Bartlett (2002), p.13 and 15

[25] see Friis-Hansen, Maganga and Sokoni (2004), p.59

[26] see Friis-Hansen, Maganga and Sokoni(2004), p.59

[27] see Owens and Simpson (2004), p.66

[28] see Anderson and Feder (2003), p.20

[29] van der Berg (2004), Chapter 5

[30] Walter-Echols (2004)

[31] see Quizon, Feder and Murgai (2004), pp.51 and 55

[32] “Do Farmer Field School Graduates Retain and Share What They Learn? An Investigation in Iloilo, Philippines”

[33] “Sending Farmers Back to School – The Impact of Farmer Field Schools in Indonesia”

[34] “The Acquisition and Diffusion of Knowledge: The Case of Pest Management Training in Farmer Field Schools, Indonesia”

[35] Feder, Murgai and Quizon (2004), p.238

[36] Rola and Jamias (2002), p.9

[37] Feder, Murgai and Quizon (2004), p.238

[38] Quizon, Feder and Murgai (2004), pp.54 and 56

[39] Quizon, Feder and Murgai (2004), p.50

[40] see Rola and Jamias (2002), p.10

[41] e.g. Waibel, Fleischer, Walter-Echols, Pemsl, Praneetvatakul or Gallagher

[42] Feder, Murgai and Quizon (2002): “Sending Farmers Back to School…”

[43] Gallagher (2002), p.3

[44] see Simpson and Owens (2002), p.5

[45] see Simpson and Owens (2002), p.2

[46] CERES-Locustox is a training and research centre for environmental toxicology, mainly of pesticides in the Sahel, PO Box 3300, Dakar, Senegal

[47] Diallo, Dieng and Everts (2004)

[48] Pemsl (2004); and Diallo, Dieng and Everts (2004)

[49] Settle (2005)

[50] Rogers (2003): “Diffusion of Innovations”

[51] Rogers (2003), p.5

[52] see Rogers (2003), p.6

[53] see Fuglie and Kascak (2001), p.388

[54] Fuglie and Kascak (2001), p.388

[55] see Fuglie and Kascak (2001), p.388

[56] Rogers (2003), p.280

[57] Rogers (2003), p.213

[58] Roeling (1988), p.64

[59] Roeling (1988), p.65

[60] Roeling (1988), p.66

[61] Asiabaka (2001), p.1

[62] see Rogers (2003), p.222

[63] Bandiera (2002), Conley (2000), Palis et al. (2002), Gerland (2004), Flanagin (2000), Nyangena (2003)


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University of Hannover – Institut für Entwicklungs- und Agrarökonomik
Diffusion Information Agriculture Senegal




Title: Diffusion of Information in Agriculture in Senegal