IP Frameworks and Innovation in the Agricultural Sector in Southeast Asia - A Legal Perspective

Abstract

<p>The ten members of the Association of Southeast Asian Nations (ASEAN) have an estimated population of nearly 680 million and over 100 million hectares of agricultural land in production. The agricultural sector only contributes around 10% of ASEAN GDP but engages approximately one-third of total ASEAN employment. The development of the agricultural sector is, therefore, critical to the sustainability of rural communities, a significant number of which are economically marginalised. There are several key findings from the research.</p> <p>Singapore, a developed country, has consistently performed within the top 10 in the World Intellectual Property Organisation's (WIPO) Global Innovation Index (GII). One of the key drivers for all three of the least developed and all six developing countries of ASEAN is to improve their intellectual property regimes so that innovation becomes a driving force in their economies. One of the critical foundations for its success in developing innovation frameworks is the long-term cooperation between ASEAN and some of its key dialogue partners, particularly Australia and New Zealand. Australia became ASEAN's first dialogue partner in 1974 and was elevated to a strategic level in 2014. Using data from ASEAN as an example, it was found that the GII, per se, is not an efficacious driver nor even a descriptor of innovation. It drives innovation if countries and industries drill down into the data. That is where the most benefit can be achieved.</p> <p>Analysis shows that each of the ten ASEAN member states, including Singapore, is a net importer of patents rather than a developer. Nonetheless, it is considered that the IP ecosystems in Malaysia, Singapore, Thailand, and Vietnam are sufficiently robust to at least consider a trial of the <i>Open Innovation, Open Science, and Open to the World</i> concept being tested in the European Union.</p> <p>Rural communities in developing economies are often marginalised due to the small size of their holdings, inferior seed stock and competition from large landholders. Sustainable production for subsistence farmers is a matter of being able to eat for the year, but they may be resistant to making any change in methods of production or crop type. Although many governments have recognised that national growth depends on innovation, they have often failed to promote or promulgate innovation in rural communities. However, there is growing worldwide interest in protecting traditional knowledge, genetic resources, plant intellectual property protection, plant variety rights, and geographical indications to leverage current knowledge and build for future development.</p> <p>The research in this thesis indicates that some Universities may be undertaking research that is potentially based on earlier traditional knowledge without acknowledging the source. Governments should be proactive and amend their patent legislation to require patent applicants to declare their use of indigenous resources and knowledge while WIPO negotiations continue.</p> <p>A suitable IP legislative framework is only a part of the solution. The suite of legislation must provide coverage for both the protection and the promotion and commercialization of traditional knowledge. In addition, there must be a suite of legislation and codes of practice addressing items such as good manufacturing practices (GMP), product safety, advertising codes of practice and trade practices.</p> <p>Finally, artificial intelligence (AI) has great potential to identify efficacious traditional medicines for commercialization by marginalized communities in tropical regions, which would provide many benefits to these communities. Regardless of the sophistication of the AI algorithms, it is incumbent on the user to undertake detailed quality checks to ensure that the data is reliable and that the source is correctly identified. Ethical issues must be addressed, such as the copyright of the underlying data, especially where the data is not open source. The advantages and pitfalls associated with using AI and a possible model for developing a comprehensive database on medicinal plants are proposed.</p>