Diffusion: Key to Horticulture Innovation Systems

Horticulture, a pillar of the Dutch economy, has already achieved remarkable productivity increases through the use of natural gas for heating, lighting and CO2. Further innovative technologies that could aid the transition toward sustainable energy use, including heat/cold storage and deepgeothermal heat sources, are currently in development and spreading. However, there is a need to better understand the processes of technology diffusion in this industrial cluster to help stakeholders retain their competitive advantage and establish the best way to influence the energy future in the region and in the sector. This presentation discusses the experimental results of a series of agent based models of the greenhouse horticulture sector in the Netherlands, simulating the technological innovation decisions of greenhouse growers. Surveys of greenhouse growers suggest that innovation decisions are made on the basis of personal experience and information shared from other growers. In the model, each greenhouse grower must learn how to operate a greenhouse by evaluating their repertoire of technologies, exchanging information with other growers about their technological evaluations and purchasing new technologies to augment, expand or replace the existing selection. The interactions of greenhouse growers and the flow of information between them lead to emergent patterns, including diversity, adaption and complexity, in the diffusion of technologies throughout the community. These emergent patterns of diffusion indicate that technological innovations develop and spread according to evolutionary mechanisms, suggesting that influencing, supporting or advocating the diffusion of sustainable technologies in this sector must also follow evolutionary mechanisms. As an evolving system, the reality of technology, innovation and transitions may require new approaches to management that work with, rather than against, the properties of evolving systems. Survey results, horticulture cluster background, model design and simulation results will be presented and implications for regional industrial management are discussed