t al (2002) provides a conducive knowledge base in which external knowledge of scientists combines with community assets leading to balancing of internal versus external diffusion of skills through a community. Community lay knowledge classification of knowledge may also differ from the classification systems used by scientists. A classical example is the different approaches used in soil classification used by farmers versus scientists. For most farmers the principle criterion by which soil is recognized and described is surface texture e.g. sand, clay or loam content. Soil surveyors on the other hand would tend to classify the soil in terms of subsurface properties (Oudwater and Martin, 2003).

Thirdly, knowledge systems should as a purpose add value to the lives of communities by continuously facilitating best practice operations. According

Introduction

Knowledge may be considered as an aggregate of knowledge systems. This aggregation includes indigenous or community lay (local or traditional) knowledge systems as well as scientific (formal ways of knowing) knowledge systems. The sum total represents the knowledge assets of a country, which are deemed essential to drive economic growth, competitive advantages, human capital, and quality of life (Malhotra, 2003). Community knowledge systems often provide different types of knowledge classifications based upon the observations, beliefs and experiences of people from a specific environmental location, a need exists to incorporate it into development plans and actions. Incorporating community knowledge sytems into developmental approaches will according to Boven and Morohashi (2002) at least enables local communities to actively participate in the decision-making process of what really works in the daily practices of people living in a specific environment. Active participation by communities contributing localized knowledge gives recognition to the Participatory Action Research approach of synergistic maximization of impact, increased citizen participation and community ownership.

Recently, more recognition is given to the fact that indigenous or community lay knowledge has intrinsic merit, and hold development potential especially for rural area development (Crithley, 2000). However, the effective application and utilization of community lay knowledge in technology development and transfer remains largely unclear despite recognition that it represents a rich body of knowledge for technological innovations.

Purpose

The purpose of this article is to provide baseline information on how community lay knowledge systems can be applied in technology development and transfer through the process of knowledge creation cooperation, education and innovation in coalition with scientific knowledge systems. Community lay knowledge for the purpose of this article is distinct from scientific knowledge is that community lay knowledge is considered to be more experiential in nature, and includes judgement and common sense which has been acquired with experience. Community lay knowledge is often referred to as practical logic (Craig, 2000).

Features of knowledge systems

Knowledge systems in general require firstly process agents that can drive change and development. In this regard Malhotra (2003) identified three process agents namely:

· National institutions; · Frameworks; and · Infrastructure.

The above three agents facilitate the effective use, sharing, creation, and renewal of knowledge required for socio-economic growth. Through knowledge systems, knowledge assets are created that eventually manifested in technologies, competencies and capabilities causing the creation of new services and products. Normally, community lay knowledge systems are severely resource constrained regarding the possession of process agents.

Secondly, knowledge systems may differ in methodology and classification. Whereas scientific knowledge systems normally represents a top-down transfer of expert knowledge from research institutions to beneficiaries (Oudwater and Martin, 2003), the filtering of community lay knowledge into the scientific body of knowledge represents a bottom-up knowledge transfer approach. This according to Ansari et al (2002) provides a conducive knowledge base in which external knowledge of scientists combines with community assets leading to balancing of internal versus external diffusion of skills through a community. Community lay knowledge classification of knowledge may also differ from the classification systems used by scientists. A classical example is the different approaches used in soil classification used by farmers versus scientists. For most farmers the principle criterion by which soil is recognized and described is surface texture e.g. sand, clay or loam content. Soil surveyors on the other hand would tend to classify the soil in terms of subsurface properties (Oudwater and Martin, 2003).

Thirdly, knowledge systems should as a purpose add value to the lives of communities by continuously facilitating best practice operations. According

ely participate in the decision-making process of what really works in the daily practices of people living in a specific environment. Active participation by communities contributing localized knowledge gives recognition to the Participatory Action Research approach of synergistic maximization of impact, increased citizen participation and community ownership.

Recently, more recognition is given to the fact that indigenous or community lay knowledge has intrinsic merit, and hold development potential especially for rural area development (Crithley, 2000). However, the effective application and utilization of community lay knowledge in technology development and transfer remains largely unclear despite recognition that it represents a rich body of knowledge for technological innovations.

Purpose

The purpose of this article is to provide baseline information on how community lay knowledge systems can be applied in technology development and transfer through the process of knowledge creation cooperation, education and innovation in coalition with scientific knowledge systems. Community lay knowledge for the purpose of this article is distinct from scientific knowledge is that community lay knowledge is considered to be more experiential in nature, and includes judgement and common sense which has been acquired with experience. Community lay knowledge is often referred to as practical logic (Craig, 2000).

Features of knowledge systems

Knowledge systems in general require firstly process agents that can drive change and development. In this regard Malhotra (2003) identified three process agents namely:

· National institutions; · Frameworks; and · Infrastructure.

The above three agents facilitate the effective use, sharing, creation, and renewal of knowledge required for socio-economic growth. Through knowledge systems, knowledge assets are created that eventually manifested in technologies, competencies and capabilities causing the creation of new services and products. Normally, community lay knowledge systems are severely resource constrained regarding the possession of process agents.

Secondly, knowledge systems may differ in methodology and classification. Whereas scientific knowledge systems normally represents a top-down transfer of expert knowledge from research institutions to beneficiaries (Oudwater and Martin, 2003), the filtering of community lay knowledge into the scientific body of knowledge represents a bottom-up knowledge transfer approach. This according to Ansari et al (2002) provides a conducive knowledge base in which external knowledge of scientists combines with community assets leading to balancing of internal versus external diffusion of skills through a community. Community lay knowledge classification of knowledge may also differ from the classification systems used by scientists. A classical example is the different approaches used in soil classification used by farmers versus scientists. For most farmers the principle criterion by which soil is recognized and described is surface texture e.g. sand, clay or loam content. Soil surveyors on the other hand would tend to classify the soil in terms of subsurface properties (Oudwater and Martin, 2003).

Thirdly, knowledge systems should as a purpose add value to the lives of communities by continuously facilitating best practice operations. According

to provide baseline information on how community lay knowledge systems can be applied in technology development and transfer through the process of knowledge creation cooperation, education and innovation in coalition with scientific knowledge systems. Community lay knowledge for the purpose of this article is distinct from scientific knowledge is that community lay knowledge is considered to be more experiential in nature, and includes judgement and common sense which has been acquired with experience. Community lay knowledge is often referred to as practical logic (Craig, 2000).

Features of knowledge systems

Knowledge systems in general require firstly process agents that can drive change and development. In this regard Malhotra (2003) identified three process agents namely:

· National institutions; · Frameworks; and · Infrastructure.

The above three agents facilitate the effective use, sharing, creation, and renewal of knowledge required for socio-economic growth. Through knowledge systems, knowledge assets are created that eventually manifested in technologies, competencies and capabilities causing the creation of new services and products. Normally, community lay knowledge systems are severely resource constrained regarding the possession of process agents.

Secondly, knowledge systems may differ in methodology and classification. Whereas scientific knowledge systems normally represents a top-down transfer of expert knowledge from research institutions to beneficiaries (Oudwater and Martin, 2003), the filtering of community lay knowledge into the scientific body of knowledge represents a bottom-up knowledge transfer approach. This according to Ansari et al (2002) provides a conducive knowledge base in which external knowledge of scientists combines with community assets leading to balancing of internal versus external diffusion of skills through a community. Community lay knowledge classification of knowledge may also differ from the classification systems used by scientists. A classical example is the different approaches used in soil classification used by farmers versus scientists. For most farmers the principle criterion by which soil is recognized and described is surface texture e.g. sand, clay or loam content. Soil surveyors on the other hand would tend to classify the soil in terms of subsurface properties (Oudwater and Martin, 2003).

Thirdly, knowledge systems should as a purpose add value to the lives of communities by continuously facilitating best practice operations. According

frastructure.

The above three agents facilitate the effective use, sharing, creation, and renewal of knowledge required for socio-economic growth. Through knowledge systems, knowledge assets are created that eventually manifested in technologies, competencies and capabilities causing the creation of new services and products. Normally, community lay knowledge systems are severely resource constrained regarding the possession of process agents.

Secondly, knowledge systems may differ in methodology and classification. Whereas scientific knowledge systems normally represents a top-down transfer of expert knowledge from research institutions to beneficiaries (Oudwater and Martin, 2003), the filtering of community lay knowledge into the scientific body of knowledge represents a bottom-up knowledge transfer approach. This according to Ansari et al (2002) provides a conducive knowledge base in which external knowledge of scientists combines with community assets leading to balancing of internal versus external diffusion of skills through a community. Community lay knowledge classification of knowledge may also differ from the classification systems used by scientists. A classical example is the different approaches used in soil classification used by farmers versus scientists. For most farmers the principle criterion by which soil is recognized and described is surface texture e.g. sand, clay or loam content. Soil surveyors on the other hand would tend to classify the soil in terms of subsurface properties (Oudwater and Martin, 2003).

Thirdly, knowledge systems should as a purpose add value to the lives of communities by continuously facilitating best practice operations. According

t al (2002) provides a conducive knowledge base in which external knowledge of scientists combines with community assets leading to balancing of internal versus external diffusion of skills through a community. Community lay knowledge classification of knowledge may also differ from the classification systems used by scientists. A classical example is the different approaches used in soil classification used by farmers versus scientists. For most farmers the principle criterion by which soil is recognized and described is surface texture e.g. sand, clay or loam content. Soil surveyors on the other hand would tend to classify the soil in terms of subsurface properties (Oudwater and Martin, 2003).

Thirdly, knowledge systems should as a purpose add value to the lives of communities by continuously facilitating best practice operations. According to Boven and Morohashi (2002) best practices using indigenous knowledge should at least possess the following characteristics:

· The practice should be innovative in nature in the sense that it has developed new and creative solutions to common problems experienced by a community; · The practice has a positive impact on the living conditions, quality of life or environment of the individuals, groups or communities concerned; · The practice should have a sustainable effect; and · The practice should have the potential to be a source of inspiration to others.

The value added role of indigenous knowledge systems can be best explained in terms of its virtues (Craig, 2000):

· This knowledge source on the common elements of mundane speech and thought, which implies broad taxonomies, simple constructions and powerful organizing metaphors and narratives. Meanings given are therefore easily generalised to many and a variety day-to-day applications; · Concepts used in community lay knowledge systems are normally broader defined than those in scientific knowledge systems leading to a situation that cause and effects relationships between variables are based upon practical logic and semantic. Application of community lay knowledge should therefore be understood within the framework, specificities and language of specific cultures; and · In application a general tendency towards mnemonic exists whereby complex process are repackaged in the memory using simple learning devices and developing simple recipes to remember it and to pass it on to next generations. The application of concepts and the resources used to fix a problem are therefore normally within the practical grasp of the user.

A need for coalition between knowledge systems exist

Community lay knowledge can complement scientific knowledge systems. However, the effective application of community lay knowledge in technology development requires in the opinion of this author a proper coalition between scientific knowledge systems and community lay knowledge systems in any development approach. In the field of innovation communities can build on and expand on their indigenous knowledge using scientific knowledge systems without forsaking local rituals, overstepping social taboos whilst giving recognition to the fact that community lay knowledge largely drives innovation in marginal and restrictive environments (Hart, 2005). Innovations that are too costly, or labour intensive are unlikely to be adopted by the community (Critchley, 2000).

But, according to Oudwater and Martin (2003) local knowledge systems should not be seen as a counterpart to scientific knowledge as it includes cultural, as well as technical knowledge and is interlinked with social and political knowledge and skills. As such these knowledge systems do indeed interact with the outside world, change over time and are thus not static systems. Local knowledge systems is however particular in terms of domain and types of knowledge. Probably, the biggest advantage is that community knowledge systems are known and installed in the daily activities of people living in a specific region (Krasilnikov and Tabor, 2003). Community knowledge can therefore be considered a highly valued knowledge resource from which scientists can extract “best elements” to combine with conventional science.

To extract best elements from and apply